# Swarm Intelligent Power-Aware Detection of Unauthorized and Compromised Nodes in MANETs

Authors: Shabana Mehfuz; M. N. Doja
Journal: Journal of Artificial Evolution and Applications
 (2008)

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## Abstract

Characteristics of mobile ad hoc networks (MANETs) such as lack of central coordination, mobility of hosts, and limited availability of resources make quality of service (QoS) provisioning very challenging. Limited resource availability such as battery power and insecure medium is one of the major QoS issues to be dealt with. In this paper, we have proposed a new secure power-aware ant routing algorithm (SPA-ARA) for mobile ad hoc networks that is inspired from ant colony optimization (ACO) algorithms which are a swarm intelligent technique. In this algorithm, we have introduced a new metric,next-hop availability, which is a combination of two metrics. It maximizes path availability and minimizes travel time of packets, and therefore it offers a good balance between selection of fast paths and a better use of network resources. The protocol also incorporates a trust model which helps in detection of unauthorized and compromised nodes in MANETs.


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## Body

## 1. Introduction

MANETs are zero-configuration, self-organizing, and
highly dynamic networks formed by a set of mobile hosts connected through
wireless links. These networks can be formed on the fly, without requiring any
fixed infrastructure. As these are infrastructureless networks, each node
should act also as a router. These characteristics of MANETs such as lack of
central coordination, mobility of hosts, limited availability of resources, and
insecure medium make quality of service (QoS) provisioning very challenging
[1]. QoS is usually defined as a set of service requirements that need to be
met by the network while transporting a packet stream from a source to its
destination(s) [2]. Providing routes that are stable based on route statistics
could potentially reduce the communication disruption time too. This can be
achieved by incorporating quality of service (QoS) metrics such as battery life
and security measures into the routing decisions as opposed to choosing a
shortest path. Efficient resource management mechanisms are required for
optimal utilization of this scarce resource, that is, battery power. Also,
MANETs are susceptible to attacks such as eavesdropping, spoofing, denial of
service, message distortion, and impersonation. Without sophisticated security
mechanisms, it is very difficult to provide secure communication guarantees.The “swarm intelligence” paradigm is an approach to
routing in distributed networks of cooperative agents, inspired by the process
by which swarms of ants converge to the optimal route to a food source by
progressively reinforcing the successful paths using pheromone secretions. ACO
and S-ACO algorithms [3] have originated from the above-mentioned paradigm and
are desirable in the context of ad hoc networks.In this paper, we have proposed a new
secure power-aware ant routing algorithm (SPA-ARA) for MANETs. It solves the
routing problem and manages the network resources from the point of view of
achieving fair resources usage across the network node. At the same time, it
also detects the unauthorized and compromised nodes in the network. It combines
the hard security mechanisms with a trust model and provides security to the
network against various internal and external attacks.The remainder of the paper is organized as follows.
Section2 briefly discusses some special characteristics of MANETs’ routing
problem. In Section 3, we describe ACO principles and ant routing algorithms
for MANETs. Section 4 discusses the power-aware routing protocols. Security
issues are discussed in Section 5. Section 6 describes the proposed algorithm.
Section 7 presents simulation. In Section 8, brief conclusions are given.


## 2. Routing Issues IN Manets

In mobile ad hoc networks, routing protocols are
challenged with establishing and maintaining multihop routes in the face of
mobility, bandwidth limitation, and power constraints. Many routing algorithms
have been designed for the mobile ad hoc networks. These algorithms can be classified into two
groups: table-driven routing (such as DSDV, CGSR, GSR, FSR, HSR, WRP, etc.)
[4, 5], and source-initiated on-demand routing (such as AODV, DSR, TORA, ABR,
SSR, etc.) [4, 6]. Both groups need large uncontrolled overheads to solve the
routing problem. The number of routing packets increases dramatically as the
network size increases. This large routing overhead affects the scalability of
the network and the network performance since it uses a significant part of the
wireless bandwidth and the node’s limited energy and processing power. In
addition, most of these algorithms are optimizing only one parameter, which in
most cases is the number of hops. In these algorithms, little work has been
done on the energy consumed during the routing. What they concerned mostly is
to find the route to make the messages reach the destination fast. In such
process, some nodes may be overused and will use out their energy quickly. So,
the network may be partitioned.These protocols are highly optimized to spread new
routing information quickly as condition changes, requiring more rapid and
often more frequent routing protocol interaction between nodes that is typical
in a traditional network. Thus, expensive and cumbersome security mechanisms
are not embedded into ad hoc routing protocols since they can delay or prevent
such exchanges of routing information, leading to reduced routing
effectiveness, and may consume excessive network or node resources, which may
lead to denial-of-service attacks through the routing protocol [7]. Sanzgiri
et al. [8] have detailed security threats against ad hoc routing protocols,
specifically examining AODV and DSR.


## 3. Ant Colony Optimization

The ACO algorithms have been inspired by the behavior of
the real ant colony. The algorithm can find the optimum solution by generating
artificial ants. As the real ants search their environment for food, the
artificial ants search the solution space. The probabilistic movement of ants
in the system allows the ants to explore new paths and to re-explore the old
visited paths. The strength of the pheromone deposit directs the artificial
ants toward the best paths, and the pheromone evaporation allows the system to
forget old information and avoid quick convergence to suboptimal solutions. A
number of proofs for the convergence to the optimum path of the ACO can be
found in [9, 10]. The ant’s behavior as well as pheromone evaporation has
been implemented in an algorithm called Simple-ACO (S-ACO) [3]. S-ACO is a
didactic tool to explain the basic mechanism underlying ACO algorithms. Some of
the important concepts in S-ACO have been explained as follows.Probabilistic forward ants and
solution construction
S-ACO ants can be thought of as having two
working modes: forward and backward. They are in forward mode when they are
moving from nest toward the food, and they are in backward mode when they are
moving from the food back to their nest. Once an ant in forward mode reaches
its destination, it switches to backward mode and starts its travel back to
source. In S-ACO forward mode, ants build a solution by
choosing probabilistically the next node to move to among those in the
neighborhood of the graph node at which they are located. Given a graphG=(N,A), where N is the set of n=|N| nodes and A is
the set of undirected arcs connecting them, two nodes i,j∈N are
neighbors if there exists an arc (i,j)∈A (see Figure 1). The
probabilistic choice is biased by pheromone trails previously deposited on the
graph by other ants. Forward ants do not deposit any pheromone trails
previously deposited on the graph by other ants. Forward ants do not deposit
any pheromone while moving. This, together with deterministic backward moves,
helps in avoiding the formation of loops.Figure 1
Connected graphG=(N,A);
N= number of nodes; A= number of arcs. Arrow shows the path where ant travels from source to destination.Deterministic backward ants and
pheromone update
The use of an explicit memory allows an ant to
retrace the path it has followed while searching the destination node.
Moreover, S-ACO ants improve the system
performance by implementing loop elimination. In practice, before starting to
move backward on the path they memorized while searching the destination node
(i.e., the forward path), S-ACO ants eliminate any loops from it. While moving
backward, S-ACO ants leave pheromone on the arcs they traverse.Pheromone updates based on solution
quality
In S-ACO, the ants memorize the nodes they
visited during the forward path, as well as the cost of the arcs traversed if
the graph is weighted. They can therefore evaluate the cost of the solutions
they generate and use this evaluation to modulate the amount of pheromone they
deposit while being in backward mode. Making pheromone update, a function of
the generated solution quality can help in directing future ants more strongly
toward better solutions. In fact, by letting ants deposit a higher amount of
pheromone on short paths, the ant’s searching is more quickly biased toward the
best solutions.Pheromone evaporation
In real ant
colonies, pheromone intensity decreases over time because of evaporation. In
S-ACO, evaporation is stimulated by applying an appropriately defined pheromone
evaporation rule. The artificial pheromone decay can be set to a constant rate.
Pheromone evaporation reduces the influence of the pheromones deposited in the
early stages of the search, when artificial ants can build poor quality
solutions.


### 3.1. Why ant colony optimization algorithm suits to MANETs

The simple ant colony optimization
metaheuristic shown illustrates different reasons why this kind of algorithms
could perform well in mobile multihop ad hoc networks. We will discuss various
reasons by considering important properties of mobile ad hoc networks.(1) Dynamic topology
This property is responsible for the bad performance of several routing
algorithms in mobile multihop ad hoc networks. The ant colony optimization
metaheuristic is based on agent systems and works with individual ants. This
allows for a high adaptation to the current topology of the network.(2) Local work
In contrast to other routing approaches, the ant colony optimization
metaheuristic is based only on local information; that is, no routing tables or
other information blocks have to be transmitted to neighbors or to all nodes of
the network.(3) Link quality
It is possible to integrate the connection/link quality into the
computation of the pheromone concentration, especially into the evaporation
process. This will improve the decision process with respect to the link
quality. It is here important to notice that the approach has to be modified so
that nodes can also manipulate the pheromone concentration independent of the
ants, that is, data packets. For this, a node has to monitor the link quality.(4) Support for multipath
Each node has a routing table with entries for all its neighbors, which
contains also the pheromone concentration. The decision rule, to select the
next node, is based on the pheromone concentration on the current node, which is
provided for each possible link. Thus, the approach supports multipath routing.


### 3.2. Ant routing algorithms for MANETs

Early work on MANETs consisted primarily of applying the
traditional approaches to routing in wired networks, such as distance vector or
link state algorithms, and
to the more volatile network environments experienced in ad hoc networks. While
many optimizations to these algorithms exist, each of them is primarily
concerned with finding the minimum hop route from source to destination.The ant routing algorithms use
artificial ant colony in the optimization as S-ACO or S-ACO metaheuristic. The ant finds, maintains, and
optimizes the best paths. The ant-colony-based routing algorithm (ARA) [11]
employs a forward/backward ant technique, which is very close to blind flooding
and is still used for path discovery. This algorithm works in an on-demand way,
with ants setting up multiple paths between source and destination at the start
of a data session. ARA is made up of two phases which are route discovery and
route maintenance. During route discovery, a forwarding ANT (FANT) is
propagated throughout the network (similar to an RREQ). At each hop, each node
calculates a pheromone value depending on how many hops the FANT has taken
to reach them. The nodes then forward the FANT to their neighbors. Once the
destination is reached, it creates a backward ANT (BANT) and returns it to the
source. When the source receives the BANT from the destination node, a path is
determined and data packet dissemination begins. To maintain each route, each
time a data packet travels between intermediate nodes, the pheromone value is
increased. Otherwise, the pheromone value is decreased over time until it
expires. To repair a broken link, the nodes firstly check their routing table;
if no route is found, they inform their neighbors for an alternate route. If
the neighbors do have a route, they inform their neighbors by backtracking. If the
source node is reached and no route is found, a new route discovery process is initiated.
The advantage of this strategy is that the size of each FANT and BANT is small,
which means that the amount of overhead per control packet introduced in the
network is minimized. However, the route discovery process is based on
flooding, which means that the protocol may have scalability problems as the
number of nodes and flows in the network grows. Probabilistic emergent routing algorithm
(PERA) [12] works in an on-demand way, with ants being broadcast toward the
destination (they do not follow pheromone) at the start of a data session.
Multiple paths are set up, but only the one with the highest pheromone value is
used by data with the other paths being available for backup. Ants-based routing
in large-scale mobile ad hoc networks [13] is another ACO algorithm in which
all nodes should be aware of their location and other nodes locations. The
algorithm protocols work in two layers: logical (upper) layer and lower layer.
The algorithm divides the network into regions and considers each region as logical
router. Each logical router’s table is distributed on the region’s nodes
satisfying some properties such as redundancy. The ant routing algorithm is
working in the logical layer. This proposed algorithm is complex because it
needs means to determine the locations of all the nodes. In addition, in each
layer, forwarding protocol is needed. Also, other ACO routing algorithms have
been proposed for MANETs in [14, 15]. Table 1 shows a summary of traditional and
ACO methods. In general, however, most of all these algorithms move quite far
away from the original ACO routing ideas trying to obtain the efficiency needed
in MANETs, and many of them are not very different from single-path on-demand
algorithms. None of ACO algorithms for MANET, mentioned above, has taken up
power-aware routing neither incorporated security measures in routing, which
are the two most major QoS issues in MANETs.Table 1
Summary of traditional and ACO methods.
ApproachProtocolsTable-driven: destination-sequenced distance vector (DSDV), cluster-head gateway switch routing (CGSR), wireless routing protocol (WRP), global state routing (GSR), optimized link state routing (OLSR), fisheye state routing (FSR), landmark routing (LMAR)TraditionalOn-demand: ad hoc on-demand distance vector (AODV), dynamic source routing (DSR), temporally ordered routing algorithm (TORA), associativity-based routing (ABR), signal stability routing (SSR)ACOAnt-colony-based routing algorithm (ARA), probabilistic emergent routing algorithm (PERA), ants-based routing in large-scale mobile ad hoc networks


## 3.1. Why ant colony optimization algorithm suits to MANETs

The simple ant colony optimization
metaheuristic shown illustrates different reasons why this kind of algorithms
could perform well in mobile multihop ad hoc networks. We will discuss various
reasons by considering important properties of mobile ad hoc networks.(1) Dynamic topology
This property is responsible for the bad performance of several routing
algorithms in mobile multihop ad hoc networks. The ant colony optimization
metaheuristic is based on agent systems and works with individual ants. This
allows for a high adaptation to the current topology of the network.(2) Local work
In contrast to other routing approaches, the ant colony optimization
metaheuristic is based only on local information; that is, no routing tables or
other information blocks have to be transmitted to neighbors or to all nodes of
the network.(3) Link quality
It is possible to integrate the connection/link quality into the
computation of the pheromone concentration, especially into the evaporation
process. This will improve the decision process with respect to the link
quality. It is here important to notice that the approach has to be modified so
that nodes can also manipulate the pheromone concentration independent of the
ants, that is, data packets. For this, a node has to monitor the link quality.(4) Support for multipath
Each node has a routing table with entries for all its neighbors, which
contains also the pheromone concentration. The decision rule, to select the
next node, is based on the pheromone concentration on the current node, which is
provided for each possible link. Thus, the approach supports multipath routing.


## 3.2. Ant routing algorithms for MANETs

Early work on MANETs consisted primarily of applying the
traditional approaches to routing in wired networks, such as distance vector or
link state algorithms, and
to the more volatile network environments experienced in ad hoc networks. While
many optimizations to these algorithms exist, each of them is primarily
concerned with finding the minimum hop route from source to destination.The ant routing algorithms use
artificial ant colony in the optimization as S-ACO or S-ACO metaheuristic. The ant finds, maintains, and
optimizes the best paths. The ant-colony-based routing algorithm (ARA) [11]
employs a forward/backward ant technique, which is very close to blind flooding
and is still used for path discovery. This algorithm works in an on-demand way,
with ants setting up multiple paths between source and destination at the start
of a data session. ARA is made up of two phases which are route discovery and
route maintenance. During route discovery, a forwarding ANT (FANT) is
propagated throughout the network (similar to an RREQ). At each hop, each node
calculates a pheromone value depending on how many hops the FANT has taken
to reach them. The nodes then forward the FANT to their neighbors. Once the
destination is reached, it creates a backward ANT (BANT) and returns it to the
source. When the source receives the BANT from the destination node, a path is
determined and data packet dissemination begins. To maintain each route, each
time a data packet travels between intermediate nodes, the pheromone value is
increased. Otherwise, the pheromone value is decreased over time until it
expires. To repair a broken link, the nodes firstly check their routing table;
if no route is found, they inform their neighbors for an alternate route. If
the neighbors do have a route, they inform their neighbors by backtracking. If the
source node is reached and no route is found, a new route discovery process is initiated.
The advantage of this strategy is that the size of each FANT and BANT is small,
which means that the amount of overhead per control packet introduced in the
network is minimized. However, the route discovery process is based on
flooding, which means that the protocol may have scalability problems as the
number of nodes and flows in the network grows. Probabilistic emergent routing algorithm
(PERA) [12] works in an on-demand way, with ants being broadcast toward the
destination (they do not follow pheromone) at the start of a data session.
Multiple paths are set up, but only the one with the highest pheromone value is
used by data with the other paths being available for backup. Ants-based routing
in large-scale mobile ad hoc networks [13] is another ACO algorithm in which
all nodes should be aware of their location and other nodes locations. The
algorithm protocols work in two layers: logical (upper) layer and lower layer.
The algorithm divides the network into regions and considers each region as logical
router. Each logical router’s table is distributed on the region’s nodes
satisfying some properties such as redundancy. The ant routing algorithm is
working in the logical layer. This proposed algorithm is complex because it
needs means to determine the locations of all the nodes. In addition, in each
layer, forwarding protocol is needed. Also, other ACO routing algorithms have
been proposed for MANETs in [14, 15]. Table 1 shows a summary of traditional and
ACO methods. In general, however, most of all these algorithms move quite far
away from the original ACO routing ideas trying to obtain the efficiency needed
in MANETs, and many of them are not very different from single-path on-demand
algorithms. None of ACO algorithms for MANET, mentioned above, has taken up
power-aware routing neither incorporated security measures in routing, which
are the two most major QoS issues in MANETs.Table 1
Summary of traditional and ACO methods.
ApproachProtocolsTable-driven: destination-sequenced distance vector (DSDV), cluster-head gateway switch routing (CGSR), wireless routing protocol (WRP), global state routing (GSR), optimized link state routing (OLSR), fisheye state routing (FSR), landmark routing (LMAR)TraditionalOn-demand: ad hoc on-demand distance vector (AODV), dynamic source routing (DSR), temporally ordered routing algorithm (TORA), associativity-based routing (ABR), signal stability routing (SSR)ACOAnt-colony-based routing algorithm (ARA), probabilistic emergent routing algorithm (PERA), ants-based routing in large-scale mobile ad hoc networks


## 4. Power-aware Routing IN Manets

As already discussed in Section2, the traditional
routing algorithms (e.g., DSDV, CGSR, GSR, FSR, HSR, WRP, etc. in [4, 5] and AODV, DSR, TORA, ABR, SSR, etc. in [4, 6]) lack power-aware routing. The
ACO algorithms which have been proposed for MANETs [11–15] mostly employ a number
of hops as the only optimization parameter, and they are not concerned with the
depleting battery power which is a very major issue involved in MANET
routing.Two classes of power-saving strategies for ad hoc
networks have been identified: local strategies, which typically operate on
small time scales, and global strategies, which operate on longer time scales
[16]. Local strategies employ the transmission power control approach, which
reduces the active communication energy by adjusting each node’s radio power
just enough to reach the receiving node but not more than that. Another
approach used by local strategy that saves the inactive energy is
sleep/power-down mode, which switches off the node when there is no data to
transmit or receive. Global strategies mainly employ load distribution approach
where the primary focus is to balance the energy usage among the nodes to
maximize the network lifetime by avoiding overutilized nodes when selecting a
routing path. An overview of some recent power-aware protocols has been given
below.Power-efficient routing protocols
include the work of Singh et al. [17] who investigated the use of power-aware
metrics in the calculation of shortest paths. These metrics describe the power
required for transmitting and receiving a packet on a link, so as to minimize
the end-to-end power requirements for routing. This proposal did not take into
account the remaining energy in the nodes, and it can result in a severe drain
of energy in the batteries of the nodes on the least-cost route.Other proposals overcame this problem
by using battery lifetime information. Toh [18] proposed a new metric, which
calculates the sum of the inverse of the remaining battery capacities of the
nodes on the path. In addition, Toh proposed the min-max algorithm to maintain
a fair use of resources by avoiding the use of nodes with the least remaining battery
capacity in the network. Li et al. [19] proposed an algorithm (denoted by max-min
zPmin) that computes the paths with minimal energy consumption while maximizing
the minimal residual power of the network.Power-aware source routing (PSR) [20]
is similar to DSR, but the destination calculates the link cost based on the
remaining battery capacity and transmission power of the nodes. The drawback of
this approach is that the destination needs to wait some time after the arrival
of the first route request, so as to receive more than one possible route, and
then selects the one with the minimum cost.


## 5. Security Issue IN Manets

As already discussed earlier,
security in MANET is one of the important QoS issues. It is an essential
component for basic network functions like packet forwarding and routing.
Network operations can be easily jeopardized if countermeasures are not
embedded into basic network functions at the early stages of their design. The
threats to the basic functions of any network such as routing have the most
immediate effects on the QoS. Unlike networks using dedicated nodes to support
basic functions like packet forwarding, routing, and network management, in ad
hoc networks those functions are carried out by all available nodes. This very
difference is at the core of the security problems that are specific to ad hoc
networks. As opposed to dedicated nodes of a classical network, the nodes of an
ad hoc network cannot be trusted for the correct execution of critical network
functions.When more security features are introduced into the
network, in parallel, along with the enhanced security strength, there are the
ever increasing computation, communication, and management overheads too. Consequently,
network performance of the security solutions, in terms of scalability, service
availability, robustness, and so on, becomes an important concern in a
resource-constrained ad hoc network. While many contemporary proposals focus on
the security vigor of their solutions from the cryptographic standpoint, they
leave the network performance aspect largely unaddressed. In fact, both
dimensions of security, that is, strength and network performance, are equally
important, and achieving a good tradeoff between two extremes is one
fundamental challenge in security design for MANETs.


### 5.1. Attacks in MANETs

The attacks in MANET can roughly be classified into two
major categories, namely, passive attacks and active attacks, according to
[21]. A passive attack obtains data exchanged in the network without disrupting
the operation of the communications, while an active attack involves
information interruption, modification, or fabrication, thereby disrupting the
normal functionality of a MANET. Table 2 shows the above-mentioned
classification of security attacks against MANET.Table 2
Security attacks classification.
Passive attacksEavesdropping, traffic analysis, monitoringActive attacksJamming, spoofing, modification, replaying, DoSThe attacks can also be classified into two external
attacks and internal attacks, according to the domain of the attacks. Some
papers call them outsider and insider attacks [22]. External attacks are
carried out by nodes that do not belong to the domain of the network. Internal
attacks are from compromised nodes, which are actually part of the network.
Internal attacks are more severe when compared with outside attacks since the
insider knows valuable and secret information and possesses privileged access
rights. Attacks can also be classified according to network protocol stacks.
Table 3 shows an example of a classification of security attacks based on
protocol stack.Table 3
Attacks on protocol stack.
LayerAttacksApplication layerRepudiation, data corruptionTransport layerSession hijacking, SYN floodingNetwork layerWormhole, black hole, Byzantine, flooding, resource consumption, location disclosure attacksData link layerTraffic analysis, monitoring, disruption MAC (802.11), WEP weaknessPhysical layerJamming, interceptions, eavesdroppingMultilayer attacksDoS, impersonation, replay, man-in-the-middleNodes that perform active attacks
with the aim of damaging other nodes by causing network outage are considered
to be malicious (also referred to as compromised), while nodes that just drop
the packets they receive with the aim of saving battery life for their own
communications are considered to be selfish [23, 24]. A selfish node affects the
normal operation of the network by not participating in the routing protocols
or by not forwarding packets. In addition, a compromised node may use the
routing protocol to advertise itself as having the shortest path to the node
whose packets it wants to intercept as in the so-called black hole attack
[25, 26].Compromised nodes can interrupt the
correct functioning of a routing protocol by modifying routing information and
by fabricating false routing information. Recent research studies have also
brought up a new type of attack that goes under the name of wormhole attack
[27, 28]. In the latter, two compromised nodes create a tunnel (or wormhole)
that is linked through a private connection, and thus they bypass the network.
This allows a node to short-circuit the normal flow of routing messages
creating a virtual vertex cut in the network that is controlled by the two
attackers [29, 30].On the other hand, selfish nodes can
severely degrade network performance and eventually partition the network by
simply not participating in the network operation. Compromised nodes can easily
perform integrity attacks by altering protocol fields in order to subvert
traffic, denying communication to legitimate nodes and compromising the
integrity of routing computations in general. Spoofing is a special case of
integrity attacks whereby a compromised node impersonates a legitimate one due
to the lack of authentication in the current ad hoc routing protocols [31, 32].
The main result of a spoofing attack is the misrepresentation of the network
topology that may cause network loops or partitioning. Lack of integrity and
authentication in routing protocols creates fabrication attacks [33–35] that
result in erroneous and bogus routing messages.Denial of service (DoS) is another
type of attack, in which the attacker injects a large amount of junk packets
into the network. These packets overspend a significant portion of network
resources, and introduce wireless channel contention and network contention in
ad hoc networks [36, 37]. In addition, the routing table overflow attack, where
an attacker attempts to create routes to nonexistent nodes, and the sleep
deprivation attack, where an attacker tries to consume the batteries of a node,
are two other types of DoS attacks [38].


### 5.2. Intrusion detection in MANETs

When a set of actions that attempt to
compromise the integrity, confidentiality, or availability of a mobile node
takes place, intrusion prevention techniques, such as encryption and
authentication, are usually the first line of defense. However, intrusion
prevention alone is not sufficient when systems become more complex. There are
two main approaches in current securing ad hoc environments. The first is intrusion
prevention measures, such as authentication and encryption. The second is
intrusion detection and response approach.


#### 5.2.1. Intrusion prevention in MANET

The secure efficient ad hoc distance (SEAD) vector
routing protocol proposed in [7] employs the use of hash chains to authenticate
hop counts and sequence numbers. SEAD is based on the design of the proactive
ad hoc routing protocol DSDV. The SEAD protocol has the utilization of a clock
synchronization mechanism or the establishment of a shared secret between each
pair of nodes as the minimum requirement. It provides loop freedom and protects
the nodes from impersonation and several other attacks.Unlike SEAD, Aridane is based on a
reactive protocol, namely, DSR, and it follows an end-to-end approach for building a
security mechanism [28]. Aridane assumes the existence of a shared secret key
between two nodes and uses a message authentication code (MAC) in order to
authenticate point-to-point messages between nodes.Zapata [39] proposed an additional routing
protocol that uses hash chains to provide security features; it is the secure
ad hoc on-demand distance vector (SAODV). SAODV proposes a set of extensions
that secures the AODV routing packets. For authenticating the nonmutable fields,
it uses cryptographic signatures, while one-way hash chains are used for
securing every different route discovery process. In order to carry out the
asymmetric cryptography, it requires the existence of a key management
mechanism. Because cryptography-based prevention techniques consume much energy
and they are invalid to internal attacks, other efficient security schemes
should be researched in ad hoc network.


#### 5.2.2. Intrusion detection and
response in MANET

Zhang and Lee [40] build on the
completely distributed structure of wireless ad hoc networks. Every node in the
network participates in the process of intrusion detection. Each node is
responsible for detecting intrusion locally and independently based on the data
collected by it. They use data on the node’s physical movements and the
corresponding change in its routing table as the trace data to build the
anomaly detection model. When the local detector finds a malicious node, it is
broadcast to the entire network. Each node also makes a final decision based on
the detection reports from other nodes. Because all the nodes run on the local
detection engine that analyzes local data for anomalies, it is too expensive to
detect some special attacks.Multiple sensors intrusion detection
system for ad hoc wireless networks based on mobile agent technology has been
proposed in [41]. Audit data is efficiently merged from multiple network
sensors, and the entire ad hoc wireless network is analyzed for intrusions, and
it is tried to inhibit intrusion attempts. A multisensor intrusion detection
system employing cooperative detection algorithm has been introduced. A mobile
agent implementation is chosen, to support such features of the IDS system as
mobility of sensors, intelligent routing of intrusion data throughout the
network, and lightweight implementation. It employs several sensor types that
perform specific certain functions, such as network monitoring, host
monitoring, decision making, and action. There are three major agent classes:
monitoring, decision-making, and action agents. Some are present on all mobile
hosts, while others are distributed to only a select group of nodes. The
monitoring agent class consists of packet, user, and system monitoring agents.
Because of scarce computational and power resources in mobile nodes, multiple
sensors and agent communication have heavy pressure on ad hoc network.


## 5.1. Attacks in MANETs

The attacks in MANET can roughly be classified into two
major categories, namely, passive attacks and active attacks, according to
[21]. A passive attack obtains data exchanged in the network without disrupting
the operation of the communications, while an active attack involves
information interruption, modification, or fabrication, thereby disrupting the
normal functionality of a MANET. Table 2 shows the above-mentioned
classification of security attacks against MANET.Table 2
Security attacks classification.
Passive attacksEavesdropping, traffic analysis, monitoringActive attacksJamming, spoofing, modification, replaying, DoSThe attacks can also be classified into two external
attacks and internal attacks, according to the domain of the attacks. Some
papers call them outsider and insider attacks [22]. External attacks are
carried out by nodes that do not belong to the domain of the network. Internal
attacks are from compromised nodes, which are actually part of the network.
Internal attacks are more severe when compared with outside attacks since the
insider knows valuable and secret information and possesses privileged access
rights. Attacks can also be classified according to network protocol stacks.
Table 3 shows an example of a classification of security attacks based on
protocol stack.Table 3
Attacks on protocol stack.
LayerAttacksApplication layerRepudiation, data corruptionTransport layerSession hijacking, SYN floodingNetwork layerWormhole, black hole, Byzantine, flooding, resource consumption, location disclosure attacksData link layerTraffic analysis, monitoring, disruption MAC (802.11), WEP weaknessPhysical layerJamming, interceptions, eavesdroppingMultilayer attacksDoS, impersonation, replay, man-in-the-middleNodes that perform active attacks
with the aim of damaging other nodes by causing network outage are considered
to be malicious (also referred to as compromised), while nodes that just drop
the packets they receive with the aim of saving battery life for their own
communications are considered to be selfish [23, 24]. A selfish node affects the
normal operation of the network by not participating in the routing protocols
or by not forwarding packets. In addition, a compromised node may use the
routing protocol to advertise itself as having the shortest path to the node
whose packets it wants to intercept as in the so-called black hole attack
[25, 26].Compromised nodes can interrupt the
correct functioning of a routing protocol by modifying routing information and
by fabricating false routing information. Recent research studies have also
brought up a new type of attack that goes under the name of wormhole attack
[27, 28]. In the latter, two compromised nodes create a tunnel (or wormhole)
that is linked through a private connection, and thus they bypass the network.
This allows a node to short-circuit the normal flow of routing messages
creating a virtual vertex cut in the network that is controlled by the two
attackers [29, 30].On the other hand, selfish nodes can
severely degrade network performance and eventually partition the network by
simply not participating in the network operation. Compromised nodes can easily
perform integrity attacks by altering protocol fields in order to subvert
traffic, denying communication to legitimate nodes and compromising the
integrity of routing computations in general. Spoofing is a special case of
integrity attacks whereby a compromised node impersonates a legitimate one due
to the lack of authentication in the current ad hoc routing protocols [31, 32].
The main result of a spoofing attack is the misrepresentation of the network
topology that may cause network loops or partitioning. Lack of integrity and
authentication in routing protocols creates fabrication attacks [33–35] that
result in erroneous and bogus routing messages.Denial of service (DoS) is another
type of attack, in which the attacker injects a large amount of junk packets
into the network. These packets overspend a significant portion of network
resources, and introduce wireless channel contention and network contention in
ad hoc networks [36, 37]. In addition, the routing table overflow attack, where
an attacker attempts to create routes to nonexistent nodes, and the sleep
deprivation attack, where an attacker tries to consume the batteries of a node,
are two other types of DoS attacks [38].


## 5.2. Intrusion detection in MANETs

When a set of actions that attempt to
compromise the integrity, confidentiality, or availability of a mobile node
takes place, intrusion prevention techniques, such as encryption and
authentication, are usually the first line of defense. However, intrusion
prevention alone is not sufficient when systems become more complex. There are
two main approaches in current securing ad hoc environments. The first is intrusion
prevention measures, such as authentication and encryption. The second is
intrusion detection and response approach.


### 5.2.1. Intrusion prevention in MANET

The secure efficient ad hoc distance (SEAD) vector
routing protocol proposed in [7] employs the use of hash chains to authenticate
hop counts and sequence numbers. SEAD is based on the design of the proactive
ad hoc routing protocol DSDV. The SEAD protocol has the utilization of a clock
synchronization mechanism or the establishment of a shared secret between each
pair of nodes as the minimum requirement. It provides loop freedom and protects
the nodes from impersonation and several other attacks.Unlike SEAD, Aridane is based on a
reactive protocol, namely, DSR, and it follows an end-to-end approach for building a
security mechanism [28]. Aridane assumes the existence of a shared secret key
between two nodes and uses a message authentication code (MAC) in order to
authenticate point-to-point messages between nodes.Zapata [39] proposed an additional routing
protocol that uses hash chains to provide security features; it is the secure
ad hoc on-demand distance vector (SAODV). SAODV proposes a set of extensions
that secures the AODV routing packets. For authenticating the nonmutable fields,
it uses cryptographic signatures, while one-way hash chains are used for
securing every different route discovery process. In order to carry out the
asymmetric cryptography, it requires the existence of a key management
mechanism. Because cryptography-based prevention techniques consume much energy
and they are invalid to internal attacks, other efficient security schemes
should be researched in ad hoc network.


### 5.2.2. Intrusion detection and
response in MANET

Zhang and Lee [40] build on the
completely distributed structure of wireless ad hoc networks. Every node in the
network participates in the process of intrusion detection. Each node is
responsible for detecting intrusion locally and independently based on the data
collected by it. They use data on the node’s physical movements and the
corresponding change in its routing table as the trace data to build the
anomaly detection model. When the local detector finds a malicious node, it is
broadcast to the entire network. Each node also makes a final decision based on
the detection reports from other nodes. Because all the nodes run on the local
detection engine that analyzes local data for anomalies, it is too expensive to
detect some special attacks.Multiple sensors intrusion detection
system for ad hoc wireless networks based on mobile agent technology has been
proposed in [41]. Audit data is efficiently merged from multiple network
sensors, and the entire ad hoc wireless network is analyzed for intrusions, and
it is tried to inhibit intrusion attempts. A multisensor intrusion detection
system employing cooperative detection algorithm has been introduced. A mobile
agent implementation is chosen, to support such features of the IDS system as
mobility of sensors, intelligent routing of intrusion data throughout the
network, and lightweight implementation. It employs several sensor types that
perform specific certain functions, such as network monitoring, host
monitoring, decision making, and action. There are three major agent classes:
monitoring, decision-making, and action agents. Some are present on all mobile
hosts, while others are distributed to only a select group of nodes. The
monitoring agent class consists of packet, user, and system monitoring agents.
Because of scarce computational and power resources in mobile nodes, multiple
sensors and agent communication have heavy pressure on ad hoc network.


## 5.2.1. Intrusion prevention in MANET

The secure efficient ad hoc distance (SEAD) vector
routing protocol proposed in [7] employs the use of hash chains to authenticate
hop counts and sequence numbers. SEAD is based on the design of the proactive
ad hoc routing protocol DSDV. The SEAD protocol has the utilization of a clock
synchronization mechanism or the establishment of a shared secret between each
pair of nodes as the minimum requirement. It provides loop freedom and protects
the nodes from impersonation and several other attacks.Unlike SEAD, Aridane is based on a
reactive protocol, namely, DSR, and it follows an end-to-end approach for building a
security mechanism [28]. Aridane assumes the existence of a shared secret key
between two nodes and uses a message authentication code (MAC) in order to
authenticate point-to-point messages between nodes.Zapata [39] proposed an additional routing
protocol that uses hash chains to provide security features; it is the secure
ad hoc on-demand distance vector (SAODV). SAODV proposes a set of extensions
that secures the AODV routing packets. For authenticating the nonmutable fields,
it uses cryptographic signatures, while one-way hash chains are used for
securing every different route discovery process. In order to carry out the
asymmetric cryptography, it requires the existence of a key management
mechanism. Because cryptography-based prevention techniques consume much energy
and they are invalid to internal attacks, other efficient security schemes
should be researched in ad hoc network.


## 5.2.2. Intrusion detection and
response in MANET

Zhang and Lee [40] build on the
completely distributed structure of wireless ad hoc networks. Every node in the
network participates in the process of intrusion detection. Each node is
responsible for detecting intrusion locally and independently based on the data
collected by it. They use data on the node’s physical movements and the
corresponding change in its routing table as the trace data to build the
anomaly detection model. When the local detector finds a malicious node, it is
broadcast to the entire network. Each node also makes a final decision based on
the detection reports from other nodes. Because all the nodes run on the local
detection engine that analyzes local data for anomalies, it is too expensive to
detect some special attacks.Multiple sensors intrusion detection
system for ad hoc wireless networks based on mobile agent technology has been
proposed in [41]. Audit data is efficiently merged from multiple network
sensors, and the entire ad hoc wireless network is analyzed for intrusions, and
it is tried to inhibit intrusion attempts. A multisensor intrusion detection
system employing cooperative detection algorithm has been introduced. A mobile
agent implementation is chosen, to support such features of the IDS system as
mobility of sensors, intelligent routing of intrusion data throughout the
network, and lightweight implementation. It employs several sensor types that
perform specific certain functions, such as network monitoring, host
monitoring, decision making, and action. There are three major agent classes:
monitoring, decision-making, and action agents. Some are present on all mobile
hosts, while others are distributed to only a select group of nodes. The
monitoring agent class consists of packet, user, and system monitoring agents.
Because of scarce computational and power resources in mobile nodes, multiple
sensors and agent communication have heavy pressure on ad hoc network.


## 6. Why Spa-ara

The application of SPA-ARA to MANETs has many advantages.
SPA-ARA is reliable, survivable, and secure routing algorithm. SPA-ARA has many
of the on-demand and table routing advantages and at the same time avoids many
of their drawbacks. In addition to that, SPA-ARA is self-built and
self-configured optimization algorithm that matches the characteristics of
MANETs. It depends on routing tables (routing pheromone table and trust
pheromone table), thus providing a high number of redundant and already
graded paths to the destination, which increases the survivability of the
algorithm. When the best path fails (due to mobility, node battery depletion,
decrease in trust value due to misbehavior, etc.), the algorithm immediately
uses the next available path. These paths increase the survivability of the
algorithm. The updating of the tables is done on demand, and it is done mainly
in the nodes, which leads to the best paths. This lowers the overhead compared
to both table-driven algorithms and on-demand algorithms. Both table-driven and
on-demand algorithms update needed and unneeded paths to the destination.To prove the validity of our proposed
algorithm, SPA-ARA, we have presented a comparison with the representative
reactive (on-demand) protocols, that is, ad hoc on-demand distance vector
(AODV) [6] and dynamic source routing (DSR) [42]. Comparison has also been
carried out with a representative ant routing protocol, ant-colony-based routing
algorithm (ARA) [11], and the comparison of security features of the proposed
protocol has been done with an on-demand secure protocol, secure routing protocol
(SRP) [43]. Salient features of all the protocols are given in the next
section.


### 6.1. AODV

AODV is an improvement on the destination-sequenced
distance-vector (DSDV) protocol. It minimizes the number of route broadcasts by
creating routes on an on-demand basis. Route discovery is initiated on an
on-demand basis; the route request is then forwarded to the neighbors, and so
on until either the destination or an intermediate node with a fresh route to
the destination is located.When
a node wants to find a route to a destination node, it
broadcasts an RREQ message with a unique RREQ ID (RID) to all its neighbors.
When a node receives an RREQ message, it updates the sequence number of source
node and sets up reverse routes to the source node in the routing tables. The
reverse route will be used to send the corresponding RREP message to the
originating node. It also updates the sequence number of the destination node
in its routing table to the maximum of the one in its routing table and the one
in the RREP message. If the node is the destination or the node has a route to
the destination that meets the freshness requirements, it unicasts an RREP back
to the source node. The source node or the intermediate node that receives RREP will update its
forward route to destination in the routing tables. Otherwise, it continues
broadcasting the RREQ. If a node receives an RREQ message that has already been
processed, it discards the RREQ and does not forward it. AODV maintains the
connectivity of neighbor nodes by sending the hello message periodically. From
this description, it is clear that the protocol is an efficient on-demand
protocol. On-demand routing protocols have been demonstrated to perform better
with significantly lower overheads than proactive routing protocols in many
scenarios since they are able to react quickly to topology changes; yet they are
able to reduce routing overhead in areas of the network in which changes are
less frequent. The other prominent on-demand protocol is DSR. A performance
comparison for the two representative on-demand protocols has been carried out
by Das et al. [44], which shows that AODV outperforms DSR in more stressful
situations, that is, with more load and larger number of nodes. Also, AODV has
been found to be more scalable and the packet delivery ratio is better in case
of AODV when compared to DSR [45]. Also, due to lack of any mechanism to expire
stale routes or to determine the freshness of routes when multiple choices are
available, DSR shows large delay and throughput performances. AODV also has commercial applications in
sensor networks and military networks. AODV routing algorithm has been
implemented on a Bluetooth-based wireless network [46]. The experimental
results show that it is possible to create a commercial application, which can
be used in many scenarios to solve practical problems. But AODV does not take
up power-aware routing and does not employ any security mechanism.


### 6.2. DSR

The key feature of DSR [42] is the
use of source routing. That is, the sender knows the complete hop-by-hop route
to the destination. These routes are stored in a route cache. The data packets
carry the source route in the packet header. When a node in the ad hoc network
attempts to send a data packet to a destination to which it does not already
know the route, it uses a route discovery process to dynamically determine such
a route. Route discovery works with flooding the network with route request
(RREQ) packets. Each node receiving an RREQ rebroadcasts it unless it is the
destination or it has a route to the destination in its route cache. Such a
node replies to the RREQ with a route reply (RREP) packet that is routed back
to the original source. RREQ and RREP packets are also source-routed. The RREQ
builds up the path traversed so far. The RREP routes itself back to the source
by traversing this path backwards. The route carried back by the RREP packet is
cached at the source for future use.If any link on the source route is
broken, the source node is notified using a route error (RERR) packet. The
source removes any route using this link from its cache. A new route discovery
process must be initiated by the source if this route is still needed.DSR makes very aggressive use of
source routing and route caching. No special mechanism to detect routing loops
is needed. Also, any forwarding node caches the source route in a packet it
forwards for possible future use. Other additional features of DSR aregratuitous
route repair and promiscuous listening. Gratuitous route repair takes place in the following manner. A source node receiving an RERR packet piggybacks
the RERR in the following RREQ. This helps in cleaning up the caches of other
nodes in the network that may have the failed link in one of the cached source
routes. By employing promiscuous hearing, when a node overhears a packet
not addressed to itself, it checks whether the packet could be routed via
itself to gain a shorter route. If so, the node sends a gratuitous RREP to the
source of the route with this new better route. Aside from this, promiscuous
listening helps a node to learn different routes without directly participating
in the routing process. DSR always shows a lower routing overhead as compared
to AODV [44].


### 6.3. SRP

We have carried out a comparative analysis of the
proposed algorithm with SRP [43]. Selection of SRP as the control algorithm for
comparing the security features of the proposed protocol is due to the
following reasons. Current efforts toward the design of secure routing
protocols are mainly oriented to on-demand protocols because of the advantages
of reactive protocols over proactive protocols already mentioned in the
previous section. Performance comparison performed on routing protocols by Bo et al. [47] shows that for both on-demand protocols AODV and DSR, throughput
declines, average packet delay is high, and the overall performance degrades
under security attacks. Therefore, SRP was selected as a candidate protocol for
comparing performance under security attacks. SRP is conceived of as an
extension that can be applied to a multitude of existing on-demand protocols.
SRP combats attacks that can disrupt the route discovery process, and
guarantees the acquisition of correct topological information. SRP mitigates
the detrimental effects of maliciously behaving nodes that disrupt the route
discovery in order to obstruct or disable the network operation. SRP provides
correct routing information, that is, factual, up-to-date, and authentic
connectivity information regarding a pair of nodes that wish to communicate in
a secure manner. In this algorithm, there exists a concept of security
association (SA) between the source node and the destination node without the
need for the intermediate nodes to cryptographically validate the control
traffic. Security association is achieved prior to the route initiation phase
through a shared key KST between the source S and target T. During
the route discovery phase, the SRP uses an additional header called SRP header
to its RREQ packet. SRP header contains the following fields: the query
sequence number QSEC, query identifier number QID, and a
96-bit message authentication code (MAC) field.When
intermediate nodes receive RREQ message, they check the SRP header. If the SRP
header is missing, they discard the message; otherwise, they forward the
message towards destination after extractingQID, source, and
destination addresses. After receiving the request packet, target T verifies if
the packet has originated from the node with which it has SA. If QSEC is greater or equal to QMAX, the request is dropped as it is
considered to be replayed. Otherwise, it calculates the keyed hash of the
request fields and if the output matches SRP MAC, then the authenticity of the
sender and the integrity of the request are verified. Upon receiving the RREP from the destination,
source checks the source address, destination address, QID, and QSEC.
It discards the RREP if it does not match the currently pending query. In case
of match, it compares reply IP source route with the exact reverse of the route
carried in reply packet. If the two routes match, then the source calculates
the MAC by using the replied route, the SRP header fields, and the secure key
between source and destination. If the two MACs match, then the validation is
successful and it confirms that the reply did come from the destination T.SRP has been proven to be essentially immune to IP
spoofing [43]. This feature is also present in SPA-ARA. But a malicious node
can harm the route that it belongs to, that is, where the proposed protocol
outperforms SRP as shown by the simulation analysis of both protocols later.


## 6.1. AODV

AODV is an improvement on the destination-sequenced
distance-vector (DSDV) protocol. It minimizes the number of route broadcasts by
creating routes on an on-demand basis. Route discovery is initiated on an
on-demand basis; the route request is then forwarded to the neighbors, and so
on until either the destination or an intermediate node with a fresh route to
the destination is located.When
a node wants to find a route to a destination node, it
broadcasts an RREQ message with a unique RREQ ID (RID) to all its neighbors.
When a node receives an RREQ message, it updates the sequence number of source
node and sets up reverse routes to the source node in the routing tables. The
reverse route will be used to send the corresponding RREP message to the
originating node. It also updates the sequence number of the destination node
in its routing table to the maximum of the one in its routing table and the one
in the RREP message. If the node is the destination or the node has a route to
the destination that meets the freshness requirements, it unicasts an RREP back
to the source node. The source node or the intermediate node that receives RREP will update its
forward route to destination in the routing tables. Otherwise, it continues
broadcasting the RREQ. If a node receives an RREQ message that has already been
processed, it discards the RREQ and does not forward it. AODV maintains the
connectivity of neighbor nodes by sending the hello message periodically. From
this description, it is clear that the protocol is an efficient on-demand
protocol. On-demand routing protocols have been demonstrated to perform better
with significantly lower overheads than proactive routing protocols in many
scenarios since they are able to react quickly to topology changes; yet they are
able to reduce routing overhead in areas of the network in which changes are
less frequent. The other prominent on-demand protocol is DSR. A performance
comparison for the two representative on-demand protocols has been carried out
by Das et al. [44], which shows that AODV outperforms DSR in more stressful
situations, that is, with more load and larger number of nodes. Also, AODV has
been found to be more scalable and the packet delivery ratio is better in case
of AODV when compared to DSR [45]. Also, due to lack of any mechanism to expire
stale routes or to determine the freshness of routes when multiple choices are
available, DSR shows large delay and throughput performances. AODV also has commercial applications in
sensor networks and military networks. AODV routing algorithm has been
implemented on a Bluetooth-based wireless network [46]. The experimental
results show that it is possible to create a commercial application, which can
be used in many scenarios to solve practical problems. But AODV does not take
up power-aware routing and does not employ any security mechanism.


## 6.2. DSR

The key feature of DSR [42] is the
use of source routing. That is, the sender knows the complete hop-by-hop route
to the destination. These routes are stored in a route cache. The data packets
carry the source route in the packet header. When a node in the ad hoc network
attempts to send a data packet to a destination to which it does not already
know the route, it uses a route discovery process to dynamically determine such
a route. Route discovery works with flooding the network with route request
(RREQ) packets. Each node receiving an RREQ rebroadcasts it unless it is the
destination or it has a route to the destination in its route cache. Such a
node replies to the RREQ with a route reply (RREP) packet that is routed back
to the original source. RREQ and RREP packets are also source-routed. The RREQ
builds up the path traversed so far. The RREP routes itself back to the source
by traversing this path backwards. The route carried back by the RREP packet is
cached at the source for future use.If any link on the source route is
broken, the source node is notified using a route error (RERR) packet. The
source removes any route using this link from its cache. A new route discovery
process must be initiated by the source if this route is still needed.DSR makes very aggressive use of
source routing and route caching. No special mechanism to detect routing loops
is needed. Also, any forwarding node caches the source route in a packet it
forwards for possible future use. Other additional features of DSR aregratuitous
route repair and promiscuous listening. Gratuitous route repair takes place in the following manner. A source node receiving an RERR packet piggybacks
the RERR in the following RREQ. This helps in cleaning up the caches of other
nodes in the network that may have the failed link in one of the cached source
routes. By employing promiscuous hearing, when a node overhears a packet
not addressed to itself, it checks whether the packet could be routed via
itself to gain a shorter route. If so, the node sends a gratuitous RREP to the
source of the route with this new better route. Aside from this, promiscuous
listening helps a node to learn different routes without directly participating
in the routing process. DSR always shows a lower routing overhead as compared
to AODV [44].


## 6.3. SRP

We have carried out a comparative analysis of the
proposed algorithm with SRP [43]. Selection of SRP as the control algorithm for
comparing the security features of the proposed protocol is due to the
following reasons. Current efforts toward the design of secure routing
protocols are mainly oriented to on-demand protocols because of the advantages
of reactive protocols over proactive protocols already mentioned in the
previous section. Performance comparison performed on routing protocols by Bo et al. [47] shows that for both on-demand protocols AODV and DSR, throughput
declines, average packet delay is high, and the overall performance degrades
under security attacks. Therefore, SRP was selected as a candidate protocol for
comparing performance under security attacks. SRP is conceived of as an
extension that can be applied to a multitude of existing on-demand protocols.
SRP combats attacks that can disrupt the route discovery process, and
guarantees the acquisition of correct topological information. SRP mitigates
the detrimental effects of maliciously behaving nodes that disrupt the route
discovery in order to obstruct or disable the network operation. SRP provides
correct routing information, that is, factual, up-to-date, and authentic
connectivity information regarding a pair of nodes that wish to communicate in
a secure manner. In this algorithm, there exists a concept of security
association (SA) between the source node and the destination node without the
need for the intermediate nodes to cryptographically validate the control
traffic. Security association is achieved prior to the route initiation phase
through a shared key KST between the source S and target T. During
the route discovery phase, the SRP uses an additional header called SRP header
to its RREQ packet. SRP header contains the following fields: the query
sequence number QSEC, query identifier number QID, and a
96-bit message authentication code (MAC) field.When
intermediate nodes receive RREQ message, they check the SRP header. If the SRP
header is missing, they discard the message; otherwise, they forward the
message towards destination after extractingQID, source, and
destination addresses. After receiving the request packet, target T verifies if
the packet has originated from the node with which it has SA. If QSEC is greater or equal to QMAX, the request is dropped as it is
considered to be replayed. Otherwise, it calculates the keyed hash of the
request fields and if the output matches SRP MAC, then the authenticity of the
sender and the integrity of the request are verified. Upon receiving the RREP from the destination,
source checks the source address, destination address, QID, and QSEC.
It discards the RREP if it does not match the currently pending query. In case
of match, it compares reply IP source route with the exact reverse of the route
carried in reply packet. If the two routes match, then the source calculates
the MAC by using the replied route, the SRP header fields, and the secure key
between source and destination. If the two MACs match, then the validation is
successful and it confirms that the reply did come from the destination T.SRP has been proven to be essentially immune to IP
spoofing [43]. This feature is also present in SPA-ARA. But a malicious node
can harm the route that it belongs to, that is, where the proposed protocol
outperforms SRP as shown by the simulation analysis of both protocols later.


## 7. Power-aware Detection Framework

SPA-ARA is a multipath protocol and
it does not maintain paths to all destinations at all times (like the ACO
algorithms for wired networks), but it sets up paths when they are needed at
the start of a session. This is done in areactive path setup phase,
where ant agents (called reactive forward ants) are launched by the
source in order to find multiple paths to the destination, and backward ants
return to set up the paths. The paths are represented in pheromone tables indicating their respective quality. After path setup, data packets are routed
stochastically as datagrams over the different paths using these pheromone
tables. The algorithm reacts to link failures by warning preceding nodes on the
paths.Every node periodically broadcasts a hello message. With this mechanism,
a node can detect its new neighbors. When a new neighbor is found, the node
checks the trust level of the neighbor and puts the neighbor along with its
trust level in itstrust pheromone table. Every node sets up a
secret key with each trustworthy neighbor by using a two-party key
establishment protocol in a manner similar to [48].The routing information of a nodeNi is represented in its routing pheromone tablePT(Ni) as shown in Table 6. The
entry TNi,Ni+1,d of a table is the pheromone value indicating the estimated goodness of
going from Ni over neighbor Ni+1 to
reach destination d. It takes into account both end-to-end delay and
number of hops. The pheromone values of each entry in the table can be
initialized to zero value, thus providing nonbiased search for the best path.
We introduce a new routing metric that takes into account the quality of links
and nodes available in a connection. We call this metric “next-hop
availability,” which is a combination of two metrics. It maximizes path
availability and minimizes travel time of packets, and therefore it offers a
good balance between selection of fast paths and a better use of network
resources. Next-hop availability is defined as the probability to find
next hops, that is, nodes and links available for routing on a path. If
pheromone information is available, and there is more than one path available
for routing, then the ant chooses that node as its next hop for which the next-hop
availability is maximum.Another pheromone table known as thetrust pheromone table TPT  (Ni) contains the trust level for all the neighbors as shown in Table 7. The entry
in the table is known as trust pheromone (Tp). Tp is a representation of the trust value of a node depending on its reliability.
At the beginning, all nodes can be trusted and they are assigned a trust value,
which is indicated as trust pheromone in the trust pheromone table of the
neighbor node. If the value of the trust pheromone is lower than a defined
threshold, the node may be considered as a malicious node.


### 7.1. Reactive path setup phase

Every node in the network can work as
a source node, destination node, and/or intermediate node. When a source nodeS starts a communication session with a destination node D, and it does
not have available routing information for S, it temporarily buffers the
data and broadcasts a reactive forward ant FSD as shown by Figure 2. The source node before broadcasting the reactive forward ant, FSD,
attaches to it a message authentication code (MAC). The MAC covers the whole
reactive forward ant and is generated by using a keyed hash algorithm [49] and
a shared group key K (to prevent modification from external attackers).
Therefore, the FSD now would be (1)Forward reactive ant:FSD∥K(MAC),where “∥” denotes concatenation and K(MAC) denotes
a
MAC generated by using key K. When a node receives an FSD and it is not the destination node, it verifies the MAC of FSD using
the shared group key K. If the MAC is correct, the node authenticates
the sender by obtaining the trust pheromone (Tp) for the sender node
from its own trust pheromone table. If the trust pheromone is
above the defined threshold value, the receiver further considers the FSD and establishes a secret key with the sender using a two-party key
establishment protocol [48]. If the trust pheromone is below the defined
threshold value, the receiver kills the ant.Figure 2
Flowchart showing initiation of
reactive forward ant.Due to this initial broadcasting,
each neighbor ofs receives a replica of FSD. We refer
to the set of replicas, which originated from the same original ant, as an ant
generation. The task of each ant of the generation is to find a path connecting S and D. At each node, an ant is either unicast or broadcast,
according to whether or not the node has routing information for S.Assume that nodeNi+1 is available for routing with a probability Pn(Ni+1) and the link (Ni,Ni+1) is available with a
probability Pl(Ni,Ni+1). Next-hop
availability Pnh(Ni,Ni+1) for the node Ni+1 from the node Ni is expressed as (2)Pnh(Ni,Ni+1)=Pn(Ni+1)×Pl(Ni,Ni+1).In real systems, many factors
contribute to the values of Pn and Pl. In this algorithm, we have expressed Pn in terms of the
remaining battery life and Pl with respect to end-to-end
delay and number of hops. The methodology for calculating both Pn and Pl is discussed later in the next sections.Due to broadcasting, ants can
proliferate quickly over the network, following different paths to the
destination. However, ants which have reached a maximum number of hops, related
to the network diameter, are killed or discarded. When a node receives several
ants of the same generation, it compares the path traveled by each ant to that
of the previously received ants of this generation. Only if its number of hops
and travel time are both less than those of the best ant of the generation, it
will forward the ant. Using this policy, overhead is limited by removing ants,
which follow bad paths. However, it does have as an effect that the ant, which
arrives first in a node, is let through, while subsequent ants meet selection
criteria set by the best of the ants preceding them. So, they have higher
chances of being killed. Duplicate ants, which result from a broadcast of the best
ant just before it reaches the destination, are close in performance to the
best ant and have higher chances of being accepted. After the above procedure,
all the nodes involved are authenticated and a secret key is set up between
each pair of neighbor nodes. The untrustworthy or suspicious nodes are excluded
in the process.Each forward ant keeps a listP of the nodes [1,…,n] it has visited. Upon arrival at the destination d,
it is converted into a backward ant BDS, which travels back
to the source retracing P (if this is not possible because the next hop
is not there, e.g., due to node movements, the backward ant is discarded). The
destination node attaches to the BDS a MAC calculated by
using the secret key that destination D shares with the next hop N for
moving towards the source. Therefore, the updated BDS would
now be (3)Backward reactive ant:BDS∥KDN(MAC).When node N receives the
reactive backward ant, it verifies the MAC. If it is correct, the routing
pheromone table entry gets updated. The trust pheromone table entry
also gets updated. The process of updating the routing pheromone table and trust pheromone table  is explained a little later. Also, N replaces the MAC in the BDS by a MAC calculated using a
secret key that it shares with the next hop obtained from the routing
pheromone table of N. Let the next hop now be M. In this case,
the updated reactive forward ant would be (4)Reactive forward ant:BDS∥KNM(MAC).At each node Ni,
the backward ant sets up a path towards the destination D, creating or
updating the value of pheromone table entry ζNi,Ni+1,d in the table PT(Ni). This process is repeated and
finally the source node receives BDS. Therefore, a number of
good secure paths are set up between the source and the destination. The entire
process is depicted in Figure 3.Figure 3
Flowchart describing node’s function.If, on the other hand, no backward ant has come back to
the source after a certain amount of time, data are temporarily buffered and
the whole process is restarted. This is repeated for a maximum number of times,
after which the buffered data are discarded.


### 7.2. Energy sensitivity

Batteries are the major source of
energy in mobile nodes. To provide greater portability, batteries need to be
small and lightweight, which unfortunately restricts the total energy that they
can carry. Once batteries exhaust their energy, they need to be replaced or
recharged, which typically reduces the independence of a mobile node for few
hours of operation.Energy consumption, in
communication-related tasks, depends on the communication mode of a node. A
node may be transmitting, receiving, or in idle mode. Naturally, transmission
consumes more energy than the other two modes. From the routing perspective,
our interest is in selecting routes in such a way that the transmission and
reception of packets are intelligently distributed on the network so as to
maximize the overall average battery lifetime of the nodes. Therefore, we are
interested in getting forward ant agents to select, with greater frequency,
those nodes which have the longest remaining battery lifetime. IfBr represents the remaining battery lifetime of node Ni,  Pn(Ni) can be expressed as (5)Pn(Ni)=Br÷Bm,where Bm is the
lifetime of a fully charged battery.Therefore, from (2) and (5), next-hop
availability Pnh(Ni,Ni+1) for the node Ni+1 from the node Ni becomes (6)Pnh(Ni,Ni+1)=Bri+1÷Bm×Pl(Ni,Ni+1).If the nodes batteries’ remaining energy is not
considered in the optimization, the best path’s node energy will be used more unfairly
than the other nodes in the network. These nodes may fail after a short time
because of their battery depletion, whereas other nodes in the network may
still have high energy in their batteries.


### 7.3. Routing pheromone table update

The backward ant incrementally
computes an estimateTP of the time it would take a data
packet to travel over the path P[1,…,n] towards the destination, which is used to update the pheromone routing
tables. TP is the sum
of local estimates T(Ni,Ni+1) in each node Ni∈P of the time to reach the next hop Ni+1, and it is given as (7)TP=∑i=1n−1T(Ni,Ni+1).T(Ni,Ni+1) is expressed as (8)T(Ni,Ni+1)=(Q(Ni)mac+1)×T(Ni)mac,where Q(Ni)mac is the number of packets in the queue at the MAC layer and T(Ni)mac is calculated as a running average of the time elapsed between the arrival of a
packet at the MAC layer and the end of a successful transmission. Since T(Ni)mac is calculated at the MAC layer, it includes channel access activities; so it
accounts for local congestion of the shared medium. Forward ants also calculate
a similar time estimate TP, which is used for filtering the
ants, as mentioned before.The pheromone table entryζNi,Ni+1,d in the table PT(Ni) of node Ni represents a running average of the inverse of the cost, in terms of both
estimated time and number of hops, to travel to destination d through
node Ni+1. If T(Ni,d) is the traveling
time estimated by the ant and h is the number of hops, the value of the
updating factor α(Ni,d), which is used to update the running
average, is expressed as (9)α(Ni,d)=((T(Ni,d)+h×Thop)÷2)−1,where Thop is a parameter representing the time to take one hop under unloaded conditions. So the pheromone table updating
factor α(Ni,d) takes into account both end-to-end delay and
number of hops. The value of ζNi,Ni+1,d is updated as (10)ζNi,Ni+1,d=γ×ζNi,Ni+1,d+(1−γ)×α(Ni,d),γ∈[0,1].


### 7.4. Trust pheromone table update

If a node in a network finds another node dropping packets, it may
consider that node malicious and decreases its trust value in itstrust
pheromone table so that the node is not able to participate in reactive
path setup phase. If a node receives BSD from a trustworthy
node, it further reinforces its trust level in its table.


### 7.5. Link availability

As explained earlier, the value of
probability for availability of a linkPl takes into account
the number of hops and end-to-end delay. It is expressed as (11)Pl(Ni,Ni+1)=ζNi,Ni+1,d÷∑j∈N′jdζNi,Nj,d,where N′jd is the set of neighbors of Ni over which a path to d is known. Therefore, from (6) and (11), we have (12)Pnh(Ni,Ni+1)=(Bri+1÷Bm)×(ζNi,Ni+1,d÷∑j∈N′jdζNi,Nj,d).


### 7.6. Protection
of data packet and routing

It is also essential to provide protection for the
data packet in an ad hoc network. For prevention of data packet modification,
each node calculates a MAC by using the secret key it shares with the next hop
and attaches the MAC to the data packet. When the next hop receives the packet,
it can verify the MAC using the secret key to check the integrity of the data
packet.Data
can be sent using many techniques. It can be sent along the maximum pheromone
path, sent following path of the best forward ant agent, or sent along the path
using maximum value of next-hop availability or a combination of them.


## 7.1. Reactive path setup phase

Every node in the network can work as
a source node, destination node, and/or intermediate node. When a source nodeS starts a communication session with a destination node D, and it does
not have available routing information for S, it temporarily buffers the
data and broadcasts a reactive forward ant FSD as shown by Figure 2. The source node before broadcasting the reactive forward ant, FSD,
attaches to it a message authentication code (MAC). The MAC covers the whole
reactive forward ant and is generated by using a keyed hash algorithm [49] and
a shared group key K (to prevent modification from external attackers).
Therefore, the FSD now would be (1)Forward reactive ant:FSD∥K(MAC),where “∥” denotes concatenation and K(MAC) denotes
a
MAC generated by using key K. When a node receives an FSD and it is not the destination node, it verifies the MAC of FSD using
the shared group key K. If the MAC is correct, the node authenticates
the sender by obtaining the trust pheromone (Tp) for the sender node
from its own trust pheromone table. If the trust pheromone is
above the defined threshold value, the receiver further considers the FSD and establishes a secret key with the sender using a two-party key
establishment protocol [48]. If the trust pheromone is below the defined
threshold value, the receiver kills the ant.Figure 2
Flowchart showing initiation of
reactive forward ant.Due to this initial broadcasting,
each neighbor ofs receives a replica of FSD. We refer
to the set of replicas, which originated from the same original ant, as an ant
generation. The task of each ant of the generation is to find a path connecting S and D. At each node, an ant is either unicast or broadcast,
according to whether or not the node has routing information for S.Assume that nodeNi+1 is available for routing with a probability Pn(Ni+1) and the link (Ni,Ni+1) is available with a
probability Pl(Ni,Ni+1). Next-hop
availability Pnh(Ni,Ni+1) for the node Ni+1 from the node Ni is expressed as (2)Pnh(Ni,Ni+1)=Pn(Ni+1)×Pl(Ni,Ni+1).In real systems, many factors
contribute to the values of Pn and Pl. In this algorithm, we have expressed Pn in terms of the
remaining battery life and Pl with respect to end-to-end
delay and number of hops. The methodology for calculating both Pn and Pl is discussed later in the next sections.Due to broadcasting, ants can
proliferate quickly over the network, following different paths to the
destination. However, ants which have reached a maximum number of hops, related
to the network diameter, are killed or discarded. When a node receives several
ants of the same generation, it compares the path traveled by each ant to that
of the previously received ants of this generation. Only if its number of hops
and travel time are both less than those of the best ant of the generation, it
will forward the ant. Using this policy, overhead is limited by removing ants,
which follow bad paths. However, it does have as an effect that the ant, which
arrives first in a node, is let through, while subsequent ants meet selection
criteria set by the best of the ants preceding them. So, they have higher
chances of being killed. Duplicate ants, which result from a broadcast of the best
ant just before it reaches the destination, are close in performance to the
best ant and have higher chances of being accepted. After the above procedure,
all the nodes involved are authenticated and a secret key is set up between
each pair of neighbor nodes. The untrustworthy or suspicious nodes are excluded
in the process.Each forward ant keeps a listP of the nodes [1,…,n] it has visited. Upon arrival at the destination d,
it is converted into a backward ant BDS, which travels back
to the source retracing P (if this is not possible because the next hop
is not there, e.g., due to node movements, the backward ant is discarded). The
destination node attaches to the BDS a MAC calculated by
using the secret key that destination D shares with the next hop N for
moving towards the source. Therefore, the updated BDS would
now be (3)Backward reactive ant:BDS∥KDN(MAC).When node N receives the
reactive backward ant, it verifies the MAC. If it is correct, the routing
pheromone table entry gets updated. The trust pheromone table entry
also gets updated. The process of updating the routing pheromone table and trust pheromone table  is explained a little later. Also, N replaces the MAC in the BDS by a MAC calculated using a
secret key that it shares with the next hop obtained from the routing
pheromone table of N. Let the next hop now be M. In this case,
the updated reactive forward ant would be (4)Reactive forward ant:BDS∥KNM(MAC).At each node Ni,
the backward ant sets up a path towards the destination D, creating or
updating the value of pheromone table entry ζNi,Ni+1,d in the table PT(Ni). This process is repeated and
finally the source node receives BDS. Therefore, a number of
good secure paths are set up between the source and the destination. The entire
process is depicted in Figure 3.Figure 3
Flowchart describing node’s function.If, on the other hand, no backward ant has come back to
the source after a certain amount of time, data are temporarily buffered and
the whole process is restarted. This is repeated for a maximum number of times,
after which the buffered data are discarded.


## 7.2. Energy sensitivity

Batteries are the major source of
energy in mobile nodes. To provide greater portability, batteries need to be
small and lightweight, which unfortunately restricts the total energy that they
can carry. Once batteries exhaust their energy, they need to be replaced or
recharged, which typically reduces the independence of a mobile node for few
hours of operation.Energy consumption, in
communication-related tasks, depends on the communication mode of a node. A
node may be transmitting, receiving, or in idle mode. Naturally, transmission
consumes more energy than the other two modes. From the routing perspective,
our interest is in selecting routes in such a way that the transmission and
reception of packets are intelligently distributed on the network so as to
maximize the overall average battery lifetime of the nodes. Therefore, we are
interested in getting forward ant agents to select, with greater frequency,
those nodes which have the longest remaining battery lifetime. IfBr represents the remaining battery lifetime of node Ni,  Pn(Ni) can be expressed as (5)Pn(Ni)=Br÷Bm,where Bm is the
lifetime of a fully charged battery.Therefore, from (2) and (5), next-hop
availability Pnh(Ni,Ni+1) for the node Ni+1 from the node Ni becomes (6)Pnh(Ni,Ni+1)=Bri+1÷Bm×Pl(Ni,Ni+1).If the nodes batteries’ remaining energy is not
considered in the optimization, the best path’s node energy will be used more unfairly
than the other nodes in the network. These nodes may fail after a short time
because of their battery depletion, whereas other nodes in the network may
still have high energy in their batteries.


## 7.3. Routing pheromone table update

The backward ant incrementally
computes an estimateTP of the time it would take a data
packet to travel over the path P[1,…,n] towards the destination, which is used to update the pheromone routing
tables. TP is the sum
of local estimates T(Ni,Ni+1) in each node Ni∈P of the time to reach the next hop Ni+1, and it is given as (7)TP=∑i=1n−1T(Ni,Ni+1).T(Ni,Ni+1) is expressed as (8)T(Ni,Ni+1)=(Q(Ni)mac+1)×T(Ni)mac,where Q(Ni)mac is the number of packets in the queue at the MAC layer and T(Ni)mac is calculated as a running average of the time elapsed between the arrival of a
packet at the MAC layer and the end of a successful transmission. Since T(Ni)mac is calculated at the MAC layer, it includes channel access activities; so it
accounts for local congestion of the shared medium. Forward ants also calculate
a similar time estimate TP, which is used for filtering the
ants, as mentioned before.The pheromone table entryζNi,Ni+1,d in the table PT(Ni) of node Ni represents a running average of the inverse of the cost, in terms of both
estimated time and number of hops, to travel to destination d through
node Ni+1. If T(Ni,d) is the traveling
time estimated by the ant and h is the number of hops, the value of the
updating factor α(Ni,d), which is used to update the running
average, is expressed as (9)α(Ni,d)=((T(Ni,d)+h×Thop)÷2)−1,where Thop is a parameter representing the time to take one hop under unloaded conditions. So the pheromone table updating
factor α(Ni,d) takes into account both end-to-end delay and
number of hops. The value of ζNi,Ni+1,d is updated as (10)ζNi,Ni+1,d=γ×ζNi,Ni+1,d+(1−γ)×α(Ni,d),γ∈[0,1].


## 7.4. Trust pheromone table update

If a node in a network finds another node dropping packets, it may
consider that node malicious and decreases its trust value in itstrust
pheromone table so that the node is not able to participate in reactive
path setup phase. If a node receives BSD from a trustworthy
node, it further reinforces its trust level in its table.


## 7.5. Link availability

As explained earlier, the value of
probability for availability of a linkPl takes into account
the number of hops and end-to-end delay. It is expressed as (11)Pl(Ni,Ni+1)=ζNi,Ni+1,d÷∑j∈N′jdζNi,Nj,d,where N′jd is the set of neighbors of Ni over which a path to d is known. Therefore, from (6) and (11), we have (12)Pnh(Ni,Ni+1)=(Bri+1÷Bm)×(ζNi,Ni+1,d÷∑j∈N′jdζNi,Nj,d).


## 7.6. Protection
of data packet and routing

It is also essential to provide protection for the
data packet in an ad hoc network. For prevention of data packet modification,
each node calculates a MAC by using the secret key it shares with the next hop
and attaches the MAC to the data packet. When the next hop receives the packet,
it can verify the MAC using the secret key to check the integrity of the data
packet.Data
can be sent using many techniques. It can be sent along the maximum pheromone
path, sent following path of the best forward ant agent, or sent along the path
using maximum value of next-hop availability or a combination of them.


## 8. Implementation AND Performance Evaluation

We have implemented the SPA-ARA
algorithm using scalable wireless ad hoc network simulator (SWANS) [50]. SWANS
is built atop the JiST [51] platform, a general-purpose discrete event
simulation engine. The size of the field was 3000×3000 m2 area.
Different number of nodes were tested. Range of each node was assumed to be 625 m. Different speeds (from 5 to 20 m/s) of movement were tested. Random mobility
was assumed with the pause time of 4 seconds. The algorithm was also tested for
different pause times, which were 0, 4, 8, 16, and 24 seconds. Pause time of 0 second corresponds to continuous motion of the node. Simulation was also
performed for varying number of malicious nodes. The algorithm was tested for
different scenes for each set of parameters. Table 4 shows the simulation
parameters. Each of the runs was repeated five times with different seeds, which resulted in a slightly changed order of events. These five runs
were merged together to build an average case, which was then used for
comparison. Therefore, each data point shown in Figures 4, 5, 6, and 7
represents an average of five runs.Table 4
Simulation parameters.
Network size3000 m× 3000 mNumber of nodes20, 49Mobility modelRandomMinimum node speed5–20 m/sPause time0, 4, 8, 16, and 24 sData rate1 message per minuteTime600 sNode’s energy standard deviation.
(a)(b)Figure 5
Successful routes found.Figure 6
Destination location time.Figure 7
Packets dropped by malicious nodes.All the nodes use batteries as their
source of energy. The energy in a battery will decrease when a packet is sent
or received. In this paper, a battery model for the “Lucent waveLAN PC card 2.4 GH direct sequence spread spectrum” [52] has been simplified and used in the
node models. From this battery model, only the power consumed by routing
(forward reactive ants, backward ants, hello messages) and data packets is
considered in this battery model. The power consumed by lower layers is not
included.The equations governing the battery
model are as follows (simplified version of [52]).When a node sends a packet(13)Node_Energy=Node_Energy−E_Send⋅PacketSize(Bytes).When a node receives a packet (14)Node_Energy=Node_Energy−E_Rec⋅PacketSize(Bytes),where (15)E_Send=2μWattSecond/Byte,E_Rec=0.5μWattSecond/Byte.


### 8.1. Fair nodes’ local energy usage
distribution

To achieve energy-efficient
communication in MANETs, different techniques have been employed which may bepower
saving routing, power control, and maximum lifetime routing. A power
saving protocol puts a node’s network interface into the sleep state in
order to save energy. Power control techniques allow nodes to modify
their transmit power to increase network capacity and reduce energy
consumption. Maximum lifetime routing increases the network lifetime by
balancing the energy consumption across the network. In MANETs, a large number of nodes
depend on limited energy sources such as batteries as their source of energy.
If the nodes batteries’ remaining energy is not considered in the optimization,
the best path’s nodes energy will be used more unfairly than the other nodes in
the network. These nodes may fail after a short time because of their battery
depletion. However, other nodes in the network may still have high energy in their batteries.SPA-ARA employs maximum lifetime routing approach to
achieve energy-efficient communication between the nodes of the network.
Therefore, the energy distribution across the network has been visualized,
which is better in SPA-ARA as compared to AODV, DSR, and ARA. So in this
approach, we are not trying to control power or save power, but we are trying
to select energy-aware routes to increase network lifetime. Therefore, SPA-ARA
has been applied to optimize the number of hops and the nodes batteries’
remaining energy. In this section, we present the application of SPA-ARA, AODV,
DSR, and ARA to 20- and 49-node networks. We will use the following definition. Energy standard
deviation is the remaining nodes’ energy standard deviation from the average
remaining energy for all the network nodes.Figures4(a) and 4(b) show the energy standard
deviation for 20 and 49 nodes, respectively. From these figures, we notice that
the maximum energy standard deviation is 48 Watt.s
in case
of AODV, 43 Watt.s in case of DSR, 47 Watt.s in case of ARA, and 37 Watt.s in
case of SPA-ARA for 20 nodes. In Figure 3(b), it is 88 Watt.s for AODV, 80 Watt.s
for DSR, 94 Watt.s for ARA, and 63 Watt.s for SPA-ARA. This is because the
energy usage is distributed across the network nodes in SPA-ARA. Table 5 shows
that the difference in maximum energy standard deviation is lower by 23% for
SPA-ARA than for AODV in one case and by 28% in another case. When compared to
DSR the difference in energy standard deviation is lower by 14% and 21%, and
when compared to ARA the difference is lower by 21% and 33%.Table 5
Maximum energy standard deviation.
ProtocolEnergy standard deviation (Watt.s)(20 nodes)(49 nodes)SPA-ARA3763AODV4888DSR4380ARA4794(AODV-SPA-ARA/AODV)%2328(DSR-SPA-ARA/DSR)%1421(ARA-SPA-ARA/ARA)%2133Table 6
Routing pheromone table at nodeNi.
DestinationNeighbor nodeNi+1Ni+2⋯Nnd1TNi,Ni+1,d1TNi,Ni+2,d1—TNi,Nn,d1d2TNi,Ni+1,d2TNi,Ni+2,d2—TNi,Nn,d2—————dnTNi,Ni+1,dnTNi,Ni+2,d2—TNi,Ni+2,d2Table 7
Trust pheromone table.
Neighbor nodeTrust pheromoneN1Tp1N2Tp2——NnTpn


### 8.2. Survivability in larger networks

The algorithm has been tested for
small as well as larger networks. We have noticed that the algorithm has the
ability to respond to dynamic changes in the network due to mobility. Figure5
shows the number of routes found during the simulation for a 49-node network.
From this figure, we can see that the number of successful routes discovered
for SPA-ARA algorithm is more than for AODV, DSR, and ARA, and it goes on increasing
with the simulation time.


### 8.3. Destination location capability

In order to compare the performance
of our proposed protocol with another secure routing protocol SRP [43], we
evaluated them with respect to the following metrics: destination location
time and number of packets dropped by malicious nodes.Figure6 presents the average time it takes an FSD to reach its destination for the first time. The route
request of the SRP propagates the request towards the destination faster than
SPA-ARA since it rejects any variant of a specific request. The FSD of the SPA-ARA has slightly longer living times than SRP. This is reasonable as
it attempts to authenticate messages by using trust levels provided in the
trust pheromone table along with the MAC attached with the messages calculated
using the shared keys.


### 8.4. Malicious node identification

As shown in Figure7, the number of packets dropped by malicious nodes using SPA-ARA routing
security scheme is less than SRP. As the number of malicious nodes increases,
the difference between SPA-ARA and SRP becomes more significant. The malicious
nodes can be identified by SPA-ARA. After this identification, their trust
pheromone in the trust pheromone tables of their neighboring nodes is decreased
below the threshold level. Therefore, there are less packets going through the
malicious nodes, and adversaries drop fewer packets maliciously. Power consumption is a critical
limitation of mobile hosts in mobile ad hoc networks. If there is a malicious
node with sufficient power supply, it can send lots of packets to attack other
nodes. Once mobile nodes receive these packets, they may have to relay these
packets or record route entries. Thus, these attacking packets may consume
power of mobile nodes. Therefore, by identification and isolation of these
malicious nodes, power consumption of mobile nodes is also reduced.


### 8.5. Power awareness and security analysis

Now, we present an analysis of how the proposed protocol
is able to select the best optimum path in terms of number of hops and power of
batteries, and at the same time provide defense against various types of
attacks.


#### 8.5.1.
Power awareness

The battery’s remaining energy is
being used for optimization, in addition to the primary optimization parameter.
If the remaining local energy in the nodes’ batteries is considered as a
parameter in the optimization, the network shown in Figure8 may respond as
follows. The path S-X-V-D has the minimum number of hops and it will be
used. When the node battery’s remaining energy in this path decreases to a
certain threshold, the S-Q-R-T-D path information will be better and the
data packets will use this path. When the remaining energy of node batteries of
this path decreases to the threshold, the data packets will switch to the S-P-Y-Z-T-D path and so on.Figure 8
An example MANET withQ as compromised node.


#### 8.5.2. External attacks

Fabrication threats from external
attackers are prevented, because the external attackers are excluded by
authentication scheme. An external attacker may try to modify a routing
message. Since external attackers do not possess the group keyK or the
secret key shared between each pair of nodes, they cannot generate correct MAC
corresponding to the message. So, modifications can be prevented.Threats using impersonation, that is,
IP spoofing, can be prevented too. There are two possibilities. First, an
attacker may impersonate a nonexistent node, but the authentication scheme will
keep it out. Second, the attacker may impersonate an existent node, for example,
nodeP in Figure 8, and replay an eavesdropped reactive forward ant.
When node Q receives this FSD, it will accept the
message if it has never received it. But when Q receives the
corresponding BDS later, Q will forward the BDS to P rather than the attacker. Thus, node P will detect the
problem if node P is in the transmission range of node Q. If P is out of Q's transmission range, the BDS is lost.


#### 8.5.3. Internal attacks

If a node is compromised, then the
attackers may control everything at the node: the shared group keyK,
the shared secret keys with other nodes, and the trust pheromone table.
The attacker can pass authentication and generate correct MAC. For example,
suppose that node Q in Figure 8 becomes a compromised node. Other nodes
in Figure 8 are good nodes. Possible attacks are analyzed as follows.(a)
Attacks on reactive
forward ant(FSD): when attacker Q receives an FSD from P, the following attacks are possible.
(i)
IP spoofing attack:
When forwardingFSD to R, Q may put an incorrect IP address in the message. Q has two choices: (1) using nonexistent IP address, which will be detected by R,
since it cannot be authenticated; (2) using existing IP address, for example,
IP address of node Y. Node R will accept it if Y is in R’s trust pheromone table and can be trusted. Otherwise, R will try to
authenticate Y and set up a key with Y. This requires Y to
be in R’s transmission range. If not, R will drop the FSD and the attack fails. But even if R accepts the FSD,
when R receives the corresponding BSD from T or V, R will forward it to Y rather than Q. There are
three possibilities. (1) Y is not participating in the current reactive
path setup. Y would detect the problem since it has never forwarded the FSD. (2) Y is a participator within the transmission range of R. The
attack helps in reactive path setup other than doing something harmful, since Y should receive a BSD from R. (3) Y is a participator
and not in the transmission range of R; the BSD will
be lost as Y cannot receive it. Therefore, the attack fails.(ii)
Modification:
AttackerQ can modify the source address (IPs) of FSD and
generate a correct MAC. When R receives the modified FSD, R will not detect it. The destination node D will not detect it
either. When Q receives the BSD from R, Q modifies it back to the correct one and then forwards it to P. The final
result is still a correct path from S to D, although it is
modified during the process of reactive path setup. So the attacker is not able
to disrupt the route discovery. An attacker can modify the destination address
(IPd) of FSD. If IPd is modified to a
nonexistent IP address, eventually the FSD will disappear. If Q modifies IPd from the IP address of D to IP
addresses of some other nodes, for example, IP address of R, node R will not find the problem. When Q receives the corresponding BSD from R, Q modifies IPd back to the IP address of node D and forwards it to P; P will accept it. So the attack has been
unsuccessful. But after Q modifies the FSD, it needs
to broadcast the modified FSD. When other nodes receive it,
they will rebroadcast it. Due to the flooding of FSD, the
original source of FSD, that is, node S, will receive
the modified FSD and detect the problem.(b)
Attacks on reactive
backward ant (BSD): when attacker Q receives BSD from R, the following attacks are possible.
(i)
IP spoofing:
WhenQ forwards the RREP to P, Q uses some other nodes’ IP addresses
instead of using its IP address to corrupt P’s routing pheromone
table. If  Q uses a nonexistent IP address, P will detect it
because there is no key shared between P and the nonexistent IP address.
If Q wants to use the IP address of an existing node, for example, IP
address of node Y, it requires that Y is in P’s trust
pheromone table, and Q needs to break the secret key shared between P and Y. This makes the attack difficult to succeed.(ii)
Modification:
AttackerQ can modify the source and destination addresses IPs and IPd
of BSD. But nodes P and S will find the problem
and drop the modified BSD, because there is no corresponding FSD.(c)
Fabrication of route
reply: when attackerQ receives a reactive path setup, Q can
forge a BSD instead of forwarding the route request to other
nodes. Q can copy the source and destination addresses from the BSD.
Since Q knows the secret keys that it shares with P and S, Q can generate the correct MAC for the BSD. So Q is able to fabricate a correct route reply and send it to P and S.
Nodes P and S will not detect the problem. They will think that
the BSD is
forwarded by Q and accept it. So an invalid path is established. But our
protocol discovers multiple paths; so there are still other paths to be used.
If there is a secret key shared between the source and destination nodes, this
problem can be prevented by utilizing a MAC calculated using the secret key.


#### 8.5.4. Message authentication

Our protocol makes use of pairwise
authentication. Each pair of neighboring intermediate nodes authenticates the
routing messages. ForFSD, two neighboring nodes
authenticate each other through the authentication process. For BSD,
a MAC generated by using the secret key shared between two neighboring nodes
provides message authenticity. If the destination node is in the trust
pheromone table of the source node or vice versa, the two ends already
establish a shared secret key. End-to-end authentication can also be utilized
besides pairwise authentication. Each reactive forward ant carries two MACs:
one for pairwise authentication and one for end-to-end authentication, which is
generated by using the secret key shared between the source and destination
nodes.


## 8.1. Fair nodes’ local energy usage
distribution

To achieve energy-efficient
communication in MANETs, different techniques have been employed which may bepower
saving routing, power control, and maximum lifetime routing. A power
saving protocol puts a node’s network interface into the sleep state in
order to save energy. Power control techniques allow nodes to modify
their transmit power to increase network capacity and reduce energy
consumption. Maximum lifetime routing increases the network lifetime by
balancing the energy consumption across the network. In MANETs, a large number of nodes
depend on limited energy sources such as batteries as their source of energy.
If the nodes batteries’ remaining energy is not considered in the optimization,
the best path’s nodes energy will be used more unfairly than the other nodes in
the network. These nodes may fail after a short time because of their battery
depletion. However, other nodes in the network may still have high energy in their batteries.SPA-ARA employs maximum lifetime routing approach to
achieve energy-efficient communication between the nodes of the network.
Therefore, the energy distribution across the network has been visualized,
which is better in SPA-ARA as compared to AODV, DSR, and ARA. So in this
approach, we are not trying to control power or save power, but we are trying
to select energy-aware routes to increase network lifetime. Therefore, SPA-ARA
has been applied to optimize the number of hops and the nodes batteries’
remaining energy. In this section, we present the application of SPA-ARA, AODV,
DSR, and ARA to 20- and 49-node networks. We will use the following definition. Energy standard
deviation is the remaining nodes’ energy standard deviation from the average
remaining energy for all the network nodes.Figures4(a) and 4(b) show the energy standard
deviation for 20 and 49 nodes, respectively. From these figures, we notice that
the maximum energy standard deviation is 48 Watt.s
in case
of AODV, 43 Watt.s in case of DSR, 47 Watt.s in case of ARA, and 37 Watt.s in
case of SPA-ARA for 20 nodes. In Figure 3(b), it is 88 Watt.s for AODV, 80 Watt.s
for DSR, 94 Watt.s for ARA, and 63 Watt.s for SPA-ARA. This is because the
energy usage is distributed across the network nodes in SPA-ARA. Table 5 shows
that the difference in maximum energy standard deviation is lower by 23% for
SPA-ARA than for AODV in one case and by 28% in another case. When compared to
DSR the difference in energy standard deviation is lower by 14% and 21%, and
when compared to ARA the difference is lower by 21% and 33%.Table 5
Maximum energy standard deviation.
ProtocolEnergy standard deviation (Watt.s)(20 nodes)(49 nodes)SPA-ARA3763AODV4888DSR4380ARA4794(AODV-SPA-ARA/AODV)%2328(DSR-SPA-ARA/DSR)%1421(ARA-SPA-ARA/ARA)%2133Table 6
Routing pheromone table at nodeNi.
DestinationNeighbor nodeNi+1Ni+2⋯Nnd1TNi,Ni+1,d1TNi,Ni+2,d1—TNi,Nn,d1d2TNi,Ni+1,d2TNi,Ni+2,d2—TNi,Nn,d2—————dnTNi,Ni+1,dnTNi,Ni+2,d2—TNi,Ni+2,d2Table 7
Trust pheromone table.
Neighbor nodeTrust pheromoneN1Tp1N2Tp2——NnTpn


## 8.2. Survivability in larger networks

The algorithm has been tested for
small as well as larger networks. We have noticed that the algorithm has the
ability to respond to dynamic changes in the network due to mobility. Figure5
shows the number of routes found during the simulation for a 49-node network.
From this figure, we can see that the number of successful routes discovered
for SPA-ARA algorithm is more than for AODV, DSR, and ARA, and it goes on increasing
with the simulation time.


## 8.3. Destination location capability

In order to compare the performance
of our proposed protocol with another secure routing protocol SRP [43], we
evaluated them with respect to the following metrics: destination location
time and number of packets dropped by malicious nodes.Figure6 presents the average time it takes an FSD to reach its destination for the first time. The route
request of the SRP propagates the request towards the destination faster than
SPA-ARA since it rejects any variant of a specific request. The FSD of the SPA-ARA has slightly longer living times than SRP. This is reasonable as
it attempts to authenticate messages by using trust levels provided in the
trust pheromone table along with the MAC attached with the messages calculated
using the shared keys.


## 8.4. Malicious node identification

As shown in Figure7, the number of packets dropped by malicious nodes using SPA-ARA routing
security scheme is less than SRP. As the number of malicious nodes increases,
the difference between SPA-ARA and SRP becomes more significant. The malicious
nodes can be identified by SPA-ARA. After this identification, their trust
pheromone in the trust pheromone tables of their neighboring nodes is decreased
below the threshold level. Therefore, there are less packets going through the
malicious nodes, and adversaries drop fewer packets maliciously. Power consumption is a critical
limitation of mobile hosts in mobile ad hoc networks. If there is a malicious
node with sufficient power supply, it can send lots of packets to attack other
nodes. Once mobile nodes receive these packets, they may have to relay these
packets or record route entries. Thus, these attacking packets may consume
power of mobile nodes. Therefore, by identification and isolation of these
malicious nodes, power consumption of mobile nodes is also reduced.


## 8.5. Power awareness and security analysis

Now, we present an analysis of how the proposed protocol
is able to select the best optimum path in terms of number of hops and power of
batteries, and at the same time provide defense against various types of
attacks.


### 8.5.1.
Power awareness

The battery’s remaining energy is
being used for optimization, in addition to the primary optimization parameter.
If the remaining local energy in the nodes’ batteries is considered as a
parameter in the optimization, the network shown in Figure8 may respond as
follows. The path S-X-V-D has the minimum number of hops and it will be
used. When the node battery’s remaining energy in this path decreases to a
certain threshold, the S-Q-R-T-D path information will be better and the
data packets will use this path. When the remaining energy of node batteries of
this path decreases to the threshold, the data packets will switch to the S-P-Y-Z-T-D path and so on.Figure 8
An example MANET withQ as compromised node.


### 8.5.2. External attacks

Fabrication threats from external
attackers are prevented, because the external attackers are excluded by
authentication scheme. An external attacker may try to modify a routing
message. Since external attackers do not possess the group keyK or the
secret key shared between each pair of nodes, they cannot generate correct MAC
corresponding to the message. So, modifications can be prevented.Threats using impersonation, that is,
IP spoofing, can be prevented too. There are two possibilities. First, an
attacker may impersonate a nonexistent node, but the authentication scheme will
keep it out. Second, the attacker may impersonate an existent node, for example,
nodeP in Figure 8, and replay an eavesdropped reactive forward ant.
When node Q receives this FSD, it will accept the
message if it has never received it. But when Q receives the
corresponding BDS later, Q will forward the BDS to P rather than the attacker. Thus, node P will detect the
problem if node P is in the transmission range of node Q. If P is out of Q's transmission range, the BDS is lost.


### 8.5.3. Internal attacks

If a node is compromised, then the
attackers may control everything at the node: the shared group keyK,
the shared secret keys with other nodes, and the trust pheromone table.
The attacker can pass authentication and generate correct MAC. For example,
suppose that node Q in Figure 8 becomes a compromised node. Other nodes
in Figure 8 are good nodes. Possible attacks are analyzed as follows.(a)
Attacks on reactive
forward ant(FSD): when attacker Q receives an FSD from P, the following attacks are possible.
(i)
IP spoofing attack:
When forwardingFSD to R, Q may put an incorrect IP address in the message. Q has two choices: (1) using nonexistent IP address, which will be detected by R,
since it cannot be authenticated; (2) using existing IP address, for example,
IP address of node Y. Node R will accept it if Y is in R’s trust pheromone table and can be trusted. Otherwise, R will try to
authenticate Y and set up a key with Y. This requires Y to
be in R’s transmission range. If not, R will drop the FSD and the attack fails. But even if R accepts the FSD,
when R receives the corresponding BSD from T or V, R will forward it to Y rather than Q. There are
three possibilities. (1) Y is not participating in the current reactive
path setup. Y would detect the problem since it has never forwarded the FSD. (2) Y is a participator within the transmission range of R. The
attack helps in reactive path setup other than doing something harmful, since Y should receive a BSD from R. (3) Y is a participator
and not in the transmission range of R; the BSD will
be lost as Y cannot receive it. Therefore, the attack fails.(ii)
Modification:
AttackerQ can modify the source address (IPs) of FSD and
generate a correct MAC. When R receives the modified FSD, R will not detect it. The destination node D will not detect it
either. When Q receives the BSD from R, Q modifies it back to the correct one and then forwards it to P. The final
result is still a correct path from S to D, although it is
modified during the process of reactive path setup. So the attacker is not able
to disrupt the route discovery. An attacker can modify the destination address
(IPd) of FSD. If IPd is modified to a
nonexistent IP address, eventually the FSD will disappear. If Q modifies IPd from the IP address of D to IP
addresses of some other nodes, for example, IP address of R, node R will not find the problem. When Q receives the corresponding BSD from R, Q modifies IPd back to the IP address of node D and forwards it to P; P will accept it. So the attack has been
unsuccessful. But after Q modifies the FSD, it needs
to broadcast the modified FSD. When other nodes receive it,
they will rebroadcast it. Due to the flooding of FSD, the
original source of FSD, that is, node S, will receive
the modified FSD and detect the problem.(b)
Attacks on reactive
backward ant (BSD): when attacker Q receives BSD from R, the following attacks are possible.
(i)
IP spoofing:
WhenQ forwards the RREP to P, Q uses some other nodes’ IP addresses
instead of using its IP address to corrupt P’s routing pheromone
table. If  Q uses a nonexistent IP address, P will detect it
because there is no key shared between P and the nonexistent IP address.
If Q wants to use the IP address of an existing node, for example, IP
address of node Y, it requires that Y is in P’s trust
pheromone table, and Q needs to break the secret key shared between P and Y. This makes the attack difficult to succeed.(ii)
Modification:
AttackerQ can modify the source and destination addresses IPs and IPd
of BSD. But nodes P and S will find the problem
and drop the modified BSD, because there is no corresponding FSD.(c)
Fabrication of route
reply: when attackerQ receives a reactive path setup, Q can
forge a BSD instead of forwarding the route request to other
nodes. Q can copy the source and destination addresses from the BSD.
Since Q knows the secret keys that it shares with P and S, Q can generate the correct MAC for the BSD. So Q is able to fabricate a correct route reply and send it to P and S.
Nodes P and S will not detect the problem. They will think that
the BSD is
forwarded by Q and accept it. So an invalid path is established. But our
protocol discovers multiple paths; so there are still other paths to be used.
If there is a secret key shared between the source and destination nodes, this
problem can be prevented by utilizing a MAC calculated using the secret key.


### 8.5.4. Message authentication

Our protocol makes use of pairwise
authentication. Each pair of neighboring intermediate nodes authenticates the
routing messages. ForFSD, two neighboring nodes
authenticate each other through the authentication process. For BSD,
a MAC generated by using the secret key shared between two neighboring nodes
provides message authenticity. If the destination node is in the trust
pheromone table of the source node or vice versa, the two ends already
establish a shared secret key. End-to-end authentication can also be utilized
besides pairwise authentication. Each reactive forward ant carries two MACs:
one for pairwise authentication and one for end-to-end authentication, which is
generated by using the secret key shared between the source and destination
nodes.


## 8.5.1.
Power awareness

The battery’s remaining energy is
being used for optimization, in addition to the primary optimization parameter.
If the remaining local energy in the nodes’ batteries is considered as a
parameter in the optimization, the network shown in Figure8 may respond as
follows. The path S-X-V-D has the minimum number of hops and it will be
used. When the node battery’s remaining energy in this path decreases to a
certain threshold, the S-Q-R-T-D path information will be better and the
data packets will use this path. When the remaining energy of node batteries of
this path decreases to the threshold, the data packets will switch to the S-P-Y-Z-T-D path and so on.Figure 8
An example MANET withQ as compromised node.


## 8.5.2. External attacks

Fabrication threats from external
attackers are prevented, because the external attackers are excluded by
authentication scheme. An external attacker may try to modify a routing
message. Since external attackers do not possess the group keyK or the
secret key shared between each pair of nodes, they cannot generate correct MAC
corresponding to the message. So, modifications can be prevented.Threats using impersonation, that is,
IP spoofing, can be prevented too. There are two possibilities. First, an
attacker may impersonate a nonexistent node, but the authentication scheme will
keep it out. Second, the attacker may impersonate an existent node, for example,
nodeP in Figure 8, and replay an eavesdropped reactive forward ant.
When node Q receives this FSD, it will accept the
message if it has never received it. But when Q receives the
corresponding BDS later, Q will forward the BDS to P rather than the attacker. Thus, node P will detect the
problem if node P is in the transmission range of node Q. If P is out of Q's transmission range, the BDS is lost.


## 8.5.3. Internal attacks

If a node is compromised, then the
attackers may control everything at the node: the shared group keyK,
the shared secret keys with other nodes, and the trust pheromone table.
The attacker can pass authentication and generate correct MAC. For example,
suppose that node Q in Figure 8 becomes a compromised node. Other nodes
in Figure 8 are good nodes. Possible attacks are analyzed as follows.(a)
Attacks on reactive
forward ant(FSD): when attacker Q receives an FSD from P, the following attacks are possible.
(i)
IP spoofing attack:
When forwardingFSD to R, Q may put an incorrect IP address in the message. Q has two choices: (1) using nonexistent IP address, which will be detected by R,
since it cannot be authenticated; (2) using existing IP address, for example,
IP address of node Y. Node R will accept it if Y is in R’s trust pheromone table and can be trusted. Otherwise, R will try to
authenticate Y and set up a key with Y. This requires Y to
be in R’s transmission range. If not, R will drop the FSD and the attack fails. But even if R accepts the FSD,
when R receives the corresponding BSD from T or V, R will forward it to Y rather than Q. There are
three possibilities. (1) Y is not participating in the current reactive
path setup. Y would detect the problem since it has never forwarded the FSD. (2) Y is a participator within the transmission range of R. The
attack helps in reactive path setup other than doing something harmful, since Y should receive a BSD from R. (3) Y is a participator
and not in the transmission range of R; the BSD will
be lost as Y cannot receive it. Therefore, the attack fails.(ii)
Modification:
AttackerQ can modify the source address (IPs) of FSD and
generate a correct MAC. When R receives the modified FSD, R will not detect it. The destination node D will not detect it
either. When Q receives the BSD from R, Q modifies it back to the correct one and then forwards it to P. The final
result is still a correct path from S to D, although it is
modified during the process of reactive path setup. So the attacker is not able
to disrupt the route discovery. An attacker can modify the destination address
(IPd) of FSD. If IPd is modified to a
nonexistent IP address, eventually the FSD will disappear. If Q modifies IPd from the IP address of D to IP
addresses of some other nodes, for example, IP address of R, node R will not find the problem. When Q receives the corresponding BSD from R, Q modifies IPd back to the IP address of node D and forwards it to P; P will accept it. So the attack has been
unsuccessful. But after Q modifies the FSD, it needs
to broadcast the modified FSD. When other nodes receive it,
they will rebroadcast it. Due to the flooding of FSD, the
original source of FSD, that is, node S, will receive
the modified FSD and detect the problem.(b)
Attacks on reactive
backward ant (BSD): when attacker Q receives BSD from R, the following attacks are possible.
(i)
IP spoofing:
WhenQ forwards the RREP to P, Q uses some other nodes’ IP addresses
instead of using its IP address to corrupt P’s routing pheromone
table. If  Q uses a nonexistent IP address, P will detect it
because there is no key shared between P and the nonexistent IP address.
If Q wants to use the IP address of an existing node, for example, IP
address of node Y, it requires that Y is in P’s trust
pheromone table, and Q needs to break the secret key shared between P and Y. This makes the attack difficult to succeed.(ii)
Modification:
AttackerQ can modify the source and destination addresses IPs and IPd
of BSD. But nodes P and S will find the problem
and drop the modified BSD, because there is no corresponding FSD.(c)
Fabrication of route
reply: when attackerQ receives a reactive path setup, Q can
forge a BSD instead of forwarding the route request to other
nodes. Q can copy the source and destination addresses from the BSD.
Since Q knows the secret keys that it shares with P and S, Q can generate the correct MAC for the BSD. So Q is able to fabricate a correct route reply and send it to P and S.
Nodes P and S will not detect the problem. They will think that
the BSD is
forwarded by Q and accept it. So an invalid path is established. But our
protocol discovers multiple paths; so there are still other paths to be used.
If there is a secret key shared between the source and destination nodes, this
problem can be prevented by utilizing a MAC calculated using the secret key.


## 8.5.4. Message authentication

Our protocol makes use of pairwise
authentication. Each pair of neighboring intermediate nodes authenticates the
routing messages. ForFSD, two neighboring nodes
authenticate each other through the authentication process. For BSD,
a MAC generated by using the secret key shared between two neighboring nodes
provides message authenticity. If the destination node is in the trust
pheromone table of the source node or vice versa, the two ends already
establish a shared secret key. End-to-end authentication can also be utilized
besides pairwise authentication. Each reactive forward ant carries two MACs:
one for pairwise authentication and one for end-to-end authentication, which is
generated by using the secret key shared between the source and destination
nodes.


## 9. Conclusion

SPA-ARA is an efficient routing
algorithm for managing the energy usage and security in MANETs. It is a dynamic routing algorithm with
controlled routing overheads. The routing packets are concentrated in the best
paths' regions. This allows for better optimization with lower number of
packets. In addition, old explored, unexplored, and bad regions are visited
with lower rate. SPA-ARA is self-built and self-configured optimization
algorithm that matches the characteristics of MANETs. The algorithm can use
different parameters in the optimization process.We have introduced a new routing
metric to cope with the limited power resources of mobile networks.Next-hop
availability, which models the probability to find the best available next
hop to be taken by the reactive ant and data packets to reach the destination,
was employed to take into account a number of parameters. These optimization
parameters were the number of hops, travel time, and the batteries’ remaining
energy. The algorithm has the ability to continuously check for better paths in
the network with controlled overheads, which make the algorithm more suitable
for network resources management. SPA-ARA combines many advantages of the on-demand-based and table-based
routing algorithms. Simulation results show the ability of the algorithm to
find the optimum solution and to achieve fair energy usage distribution as an
example of network resources management.At each node, there exists arouting
pheromone table as well as trust pheromone table. The trust
pheromone table gets updated according to the trustworthiness possessed by
a neighbor. This trust level helps in isolating malicious and compromised nodes
present in the network. Integrity is ensured by MAC, which is calculated using
a shared key in forward path setup and pairwise shared secret keys in the
backward path. The protocol can detect most of the attacks, which are common to
ad hoc network routing protocols. Moreover, the protocol is capable of
discovering secure paths
as well as the most power-optimized path for dynamically changing topology.



---

*Source: 236803-2008-02-07.xml*