DEIM Forum 2017 H2-4

モバイルアドホックネットワークにおける機械学習を用いた攻撃端末特定手法
Boqi GAO† , Takuya MAEKAWA† , Daichi AMAGATA† , and Takahiro HARA†† Osaka University, Graduate School of Information Science and Technology
1–5, Yamadaoka, Suita, Osaka 565–0871 Japan
E-mail: †{gao.boqi,maekawa,amagata.daichi,hara}@ist.osaka-u.ac.jp
Abstract モバイルアドホックネットワーク（MANETs）は，既存のインフラを利用せず，無線モバイル端末により自律的に構築されることから，災害などの緊急事態における利用形態として注目されている．しかし，端末が自律的に構築する利点は，攻撃端末の参入を許してしまうという欠点もあり，セキュリティ面の課題に直面する．MANETs
では，これまでに多くの攻撃モデルが考えられてきたが，既存研究は一つのモデルにのみ着目し，複数の攻撃モデルが同時に存在する環境については考えられていない．本稿では，複数の攻撃モデルが同時に存在する環境においても攻撃端末を特定することを目的とし，機械学習に基づく手法を提案する．提案手法では，ルーティングプロトコルとして AODV を使用することを想定し，パケットの送受信率などから通常端末と攻撃端末を識別する分類機を作成する．シミュレーション実験の結果から，提案手法はどのような攻撃モデルが存在していても高精度で攻撃端末を特定できることを確認した．

Key words MANETs, security, machine learning, classifier

1. Introduction

neighbor nodes to normal and malicious nodes. Note that
network parameters, such as number of nodes, mobile veloc-

Mobile ad-hoc networks (MANETs) have been receiving

ity, and network size, are unknown, because of self-organizing

much research interests [7], due to their self-organizing andcharacteristic. We therefore have to design a robust classifier

infrastructure-less characteristics. In MANETs, specifically,

to network parameters, which is also challenging.

nodes move freely and use multi-hop messages to communi-

In this paper, we propose a method which builds a robust

cate with each other. Since this kind of infrastructure-lessclassifier to environments of multiple attacks and networks

networks do not require base stations to transmit messages,

with diﬀerent parameters. To summarize, the contributions

they are applicable in many fields, e.g., emergency corre-of this paper are as follows.

spondence and rescuing work. Due to that characteristic,（ 1 ） We classify famous attacks in MANETs into four

however, MANETs are vulnerable to malicious nodes. If

categories. We select and test useful features based on themalicious nodes join a MANET, or if some normal nodes

classification.

are hacked by adversaries, the MANET suﬀers from their（ 2 ） We propose a method that builds a robust classifierattacks. That is, normal nodes encounter cases that commu-

to classify neighbor nodes based on their behaviors.

nications result in failures.
So far, many attack models, such as black hole attack [5],（ 3 ） We propose a weighting mechanism of ensemble

learning for identifying malicious nodes.

rushing attack [10], and sybil attack [1], have been consid-（ 4 ） Our simulation result shows that our method canered. It is important to identify malicious nodes which exe-

identify malicious nodes with high accuracy.

cute the above attacks, for secure communications. Existing

The organization of this paper is as follows. In Section

literatures assume that there is only a single attack model in

2, we introduce the preliminary of our work including as-a MANET. This is obviously unrealistic, because some ma-

sumption, attack models, and related work. In Section 3, we

licious nodes do an attack, and some other malicious nodes

present our proposed method that builds a robust classifier.

do diﬀerent attacks. We thus address a challenging problem

We present our result of simulation experiments in Section 4.

of identifying malicious nodes in MANETs where multiple

Finally, in Section 5, we conclude this paper and introduce

attack models exist.

future work.

A promising solution is to build a classifier and apply it oneach node. Nodes can utilize the classifier to classify their2. Preliminary

Routing packet dropping attack: In routing packet
dropping attack, a malicious node randomly drops routing

2. 1 Assumptions

messages. For example, in AODV, a malicious node ran-

The environment is assumed to be a mobile ad-hoc network

domly drops RReqs, RReps, and Rerrs.

(MANET) constructed by wireless mobile nodes. These mo-

Rushing attack [10]: Rushing attack is mainly against on-

bile nodes move freely and their communication range is thedemand routing protocols. When a malicious node receives

same. As a routing protocol, AODV [16] is employed. AODVan RReq from its neighbor node, it broadcasts the RReq

is an on-demand routing protocol. AODV determines a routequickly over the network before other nodes do. They thus

to a destination only when a node wants to send a packet

select it as a target for RRep.

to the destination, using four message types: route request

Wormhole attack [9]: In wormhole attack, a malicious(RReq), route reply (RRep), route error (Rerr), and hellonode stores all received messages, and send them to anothermessage. In AODV, if a source node wants to send a data

faraway malicious node in their own channel, for example, apacket to a destination node and there is no existing route

wired link or an out of band hidden channel. The other ma-

in its routing table, the source node broadcasts an RReq. Its

licious node which receives these messages broadcasts themneighbour node which receives the RReq checks whether it

locally. This attack creates an illusion that the neighboris the destination node. If so, it sends an RRep to the orig-

nodes of these two malicious nodes are very close to each

inator of the RReq. If not, it broadcasts the RReq. Other

other.

nodes which receive the RReq do the same procedure until

Jelly fish delay attack [15]: In jelly fish delay attack, ma-

the destination node receives the RReq. Nodes monitor the

licious nodes hold received messages for a while before trans-

link status of next hops in active routes. When a node de-mission.

tects a link failure in an active route, it sends an Rerr to

Flooding attack [12]: Flooding attack is an attack for en-

notify other nodes of the link failure. Moreover, each nodeergy consumption. This attack has many patterns. One

checks whether it has sent a message or not within an inter-

famous pattern is called RReq flooding attack. In RReq

val called hello interval. If it has not, it broadcasts a hello

flooding attack, a malicious node broadcasts an RReq with-message to notify other nodes of its existence.

out destination. Because all nodes transmit this message,

Besides, there could be some malicious nodes in the

redundant traﬃc incurs.

MANET. That is, some adversaries may join the MANET.

2. 3 Related work

Their objective is to interrupt communications in theClassification is an eﬀective way to detect malicious nodes

MANET by executing attacks introduced in Section 2.2. We[19]. It has been extensively used for intrusion detection inassume that malicious nodes can change their attack models.

wired networks. However, few works investigated the capa-

2. 2 Attack models

bility of classification approach for malicious node detection

We introduce attacks executed by malicious nodes. The

in MANETs.

following attacks are famous attacks in existing researches

Literature [13] conducted some experiments that compare

about MANETs security.

the performances of five well known classifiers: Naive Bayes,

Black hole attack [5]: In black hole attacks, a malicious

MLP, Linea, GMM and SVMs. However, [13] considers only

node acts like a black hole in universe. By sending fakefour attacks, and does not investigate the robustness of clas-

RReps to all RReqs, the malicious node pretends to have asifiers to network parameters, such as area size and number ofroute to a given destination. This makes its neighbor nodes

nodes. In [14], Nadeem and Howarth presented an intrusion

send packets to it. After receiving data packets, the mali-

detection and adaptive response mechanism for MANETs.

cious node drops them.

This detects a range of single attacks and provides an eﬀec-

Grey hole attack [17]: The grey hole attack is just liketive response to attacks. However, it cannot detect malicious

black hole attack. The diﬀerence between black hole attack

nodes which execute multiple attacks at the same time.

and grey hole attack is that in grey hole attack, the malicious

Literature [2] proposed a machine learning based reputa-

node drops data packets with a certain probability.

tion system for MANETs, and considered digital signature

Sybil attack [1]: In sybil attack, a malicious node performs

based mechanisms that do not require trusted third parties.

like some other nodes. Malicious nodes send RReps while

A new technique, called dynamic thresholds was also pro-pretending specific normal nodes. In such a situation, nodes

posed, to improve classification accuracies. This work, how-around the malicious nodes consider the malicious nodes asever, did not test the system on the environment of multiple

normal nodes. Packets are consequently dragged to the ma-

attacks and did not investigate the scalability to network

licious nodes, which disturbs packet transmissions.

parameters.

3. Proposed method

Table 2 Definition of information that observing node obtains
Information Definition
NReqRec# RReq overheard from one neighbor

NRepRec# RRep overheard from one neighbor

NRepSen# RRep sent to one neighbor

NRerRec# Rerr overheard from one neighborfrom an ensemble classifier which is created by weights and

NRerSen# Rerr sent to one neighbor

all the weak classifiers.

In this section, we present our method for building a robust
classifier. We build weak classifiers and calculate a weight foreach weak classifier. Finally, classification result is obtained

NHelRec# hello message overheard from one neighbor

3. 1 Building weak classifiers

NDatRec# data packets overheard from one neighborTo build weak classifiers, we consider useful features, and

NDatSen# data packets sent to one neighbor

the features are obtained by simulations. Features are key

TReqRec

total # RReq observing node overhears

elements in classification approaches, and selecting features

TReqSen

total # RReq sent by observing node

TRepRec

total # RRep observing node overhears

TRepSen

total # RRep sent by observing node

TRerRec

total # Rerr observing node overhears

TRerSen

total # Re-rr sent by observing node

THelRec

total # hello message observing node overhears

Classification of attacks. As discussed before, a large

THelSen

total # hello message sent by observing node

number of attacks have been considered in MANETs, and

TDatRec

total # data packets observing node overhears

lots of studies classify attacks based on layers or routing pro-

TDatSen

total # data packets sent by observing nodeis important to build a robust classifier. To consider and select such useful features, we first classify the attack models
to some categories.
3. 1. 1 Getting features

tocols [3]. However, these classifications do not help building
a classifier because they do not classify the attacks based on
their characteristics. We therefore classify the attacks based

Table 3 Behavior features
Behavior features Definition
RepSenRatio

NRepRec / TRepRecon their objectives, namely (i) causing link failure (CLF),

RepRecRatio

NRepRec / TReqRec(ii) dropping data packets (DDP), (iii) dragging routes to

RepIgnRatio

NRepRec / NRepSen

attackers (DRA), and (iv) causing redundant traﬃc (DRT).

ReqRecRatio

NReqRec / TReqRec

The result of our classification of attacks is summarized

DatSenRatio

NDatSen / TDatSenin Table 1. Note that some attacks, such as black hole and

DatRecRatio

NDatRec / TDatRec

sybil attacks, are categorized into multiple classes. For ex-

DatIgnRatio

NDatRec / TDatSen

RerRecRatio

NRerRec / TRerRec

HelRecRatio

NHelRec / THelRec

AllPckRatio(NDatSen+NRepSen) / (NDatRec+NRepRec)

RepUslRatio

NDatRec / NRepSen

RepReqRatio

NRepRec / TReqSen

RerSenRatio

NRerSen / TRerSen

HelCheckRatio

NHelRec / THelSen

ample, in black hole attack, malicious nodes send RReps anddrop data packets. This attack has two objectives: packet
dropping and route dragging.

Class

Table 1 Classification of attacks
Attacks

CLF

Routing packet dropping, sybil attack

DDP

Black hole attack, grey hole attack

DRA
DRT

Sybil attack, rushing attack, wormhole attack,cation of attacks. It can be seen that we use ratios calculated

black hole attackfrom the information that observing nodes hold. The reason

RReq flooding attack, hello flooding attack

why we select ratios as features is that ratios are not influenced by network operation time. For example, a network

In our work, a classifier is set on each node. That is, each

operated for 100 seconds would have diﬀerent total packet

node works as an observing node and classifies its neighbors.

transmission numbers to a network operated for 300 seconds.

Before we introduce the features we use, we introduce theThat is, using number of packets is not suitable for diﬀerent

information that each node observes in Table 2. This infor-

network situations. The relationship between behavior fea-

mation is used to create the features. Recall that in AODV,

tures and the classifications are illustrated below.

messages are transmitted by broadcast. Observing nodes
thus can overhear the messages.

RRep sent ratio (RepSenRatio), RRep ignored ratio(RepIgnRatio), Rerr sent ratio (RerSenRatio), and Rerr re-In our method, we employ two kinds of features: behavior

ceived ratio (RerRecRatio) are designed for CLF. A link fail-features for classifying neighbors and environment features

ure is caused by network topology change and ignoring rout-for estimating network environments.

ing messages such as RReps. If routing messages are ignored

Behavior features. In Table 3, we define 14 features as be-by malicious nodes, normal nodes cannot update their rout-havior features. These features are designed from the classifi-

ing tables. Normal nodes thereby cannot transmit messages

number of normal nodes. Therefore, normal nodes typicallyat the latest network topology. To deal with this problem,

observe the behaviors of other normal nodes. This means

ratios about the received and sent routing messages are de-that an observing node obtains less information on mali-signed to check whether neighbour nodes drop routing mes-

cious nodes compared with that on normal nodes. Such im-sages or not.

balanced information (data) influences the performances ofData received ratio (DatRecRatio), Data ignored ratio(DatIgnRatio), and RRep useless ratio (RepUslRatio) areclassification algorithms [8]. We hence need to alleviate theinfluence of imbalanced data.

designed for DDP. We can know situations that neighbour

There are two ways to deal with imbalanced data: over-nodes transmit data packets properly, from the ratios cal-sampling and under-sampling. Under-sampling is used in ourculated from the number of overheard data packets. For

method because suﬃcient data can be obtained from simula-

example, if the DatIgnRatio of one neighbor node is muchtions. Information on normal nodes would be similar, mean-

lower than those of other neighbour nodes, it is reasonable toing that important information will not lose even if someconsider that this neighbor node ignores some data packets.

normal nodes’ information is cut oﬀ. We use spread sub-Data packet sent ratio (DatSenRatio), RRep checked bysample [4] as the under-sampling method. Spread subsample

RReq ratio (RepReqRatio), RRep received ratio (RepRecRa-

is a method to generate a random subsample of a dataset.

tio), and all routing packets ratio (AllPckRatio) are designed

The ratio of instances (vectors consisting of behavior andfor DRA. The reason is that if one malicious node wants toenvironment features) obtained from normal nodes and ma-

drag routes to itself, it will send more RReps and receive

licious nodes, which are used for training, is controlled tomany data packets.

1:1.

RReq received ratio (ReqRecRatio), Hello check ratio

3. 1. 3 Classifier building(HelCheckRatio), and Hello message received ratio (Hel-In our approach, to build a weak classifier, we employ ran-

RecRatio) are designed for DRT. Because malicious nodes,

dom forest [11]. Random forest is an improvement method

which execute flooding attacks, send extra hello messages

over bagged decision trees. Random forest can avoid over

and/or RReqs to incur redundant traﬃc, the features are

fitting even without pruning because it employs the column

useful to identify such malicious nodes.

and instance random sampling method. Random forest canEnvironment features. We next define environment fea-

deal with a large number of instances, hence is useful to cre-tures in Table 4. We define a fixed interval time as a window

ate a highly accurate classifier. Recall that we use behavior

time. In a nutshell, each observing node records the num-and environment features. Behavior features are ratios whilebers of sent and received packets in a window time. Let xi

environment features are numbers. Decision trees are useful

be a number that each observing node counts in a window

to deal with both values.

time. Assume that we have ω window times, then each node∑

obtains an environment feature, i.e.,xi.
ωUsing random forest has three advantages, (i) fast train-

All environment

ing time, (ii) capability of dealing with a huge number offeatures are created by this approach, and the environment

instances, (iii) and accelerating eﬃciency, due to a large num-features are utilized to estimate the network environments.

ber of decision trees.
3. 2 Calculation of weight

Table 4 Summary of environment features
Env. features Definition

It is intuitively known that similar network settings result in similar mean of environment features, and diﬀerent

AvgReqRec

Avg. # RReq observing node overhears

Evergreen

Avg. # RReq sent by observing node

network settings result in diﬀerent mean of environment fea-

AvgRepRec

Avg. # RRep observing node overhears

tures. From contraposition, similar mean of environment

AvgRepSen

Avg. # RRep sent by observing nodefeatures result in similar network settings. Also, similar net-

AvgDatRec

Avg. # data packets observing node overhears

work settings result in similar classifier models. That is, if

AvgDatSen

Avg. # data packets sent by observing node

two given networks have similar mean of environment fea-

AvgRerRec

Avg. # Rerr observing node overhears

tures, the classifiers of these two networks are similar.

AvgRerSen

Avg. # Rerr sent by observing node

AvgHelRec

Avg. # hello messages observing node overhears

AvgHelSen

Avg. # hello messages sent by observing node

AvgNeiMet

Avg. # neighbor nodes

To validate this, we conduct a preliminary experiment.
This experiment investigates the influence of diﬀerent network environments on environment features and rank of behavior features. We employ four network parameters: max

3. 1. 2 Under-sampling
The number of malicious nodes is generally less than the

speed of nodes vmax , area size, ratio of malicious nodes, andnumber of nodes. As an example, we show the influence ofvmax , on environment features. Area size, ratio of malicious

Table 5 Rank of behavior features with diﬀerent vmax
vmax = 1.0

120vmax = 2.0

vmax = 3.0

vmax = 4.0

Mean of environment features

100

80
60

R. vmax = 1.0

vmax = 2.0

vmax = 3.0

1

RepRecRatio

RepRecRatio

RepReqRatio RepSenRatio

2

RepSenRatio

RepReqRatio

RepSenRatio

AllPckRatio

3

RepReqRatio RepSenRatio

AllPckRatio

RepUslRatio

4

RepUslRatio

HelCheckRatio RepIgnRatio

RepReqRatio

5

DatRecRatio

RepIgnRatio

DatRecRatio

RepRecRatio

vmax = 4.0

40

classifiers to fit network parameters. Calculation of weight20

helps finding similar network settings of the weak classifiers.
0
AvgReqSen

AvgDatSen

AvgRerRec

AvgRerSen

AvgHelSen

AvgNeiMet

The principal weight calculation is that the more similar theenvironment features of a given instance and mean of envi-

Figure 1 Diﬀerent vmax to environment features (1)ronment features of a training data are, the larger the weightis.
Given the environment features of a test instance t, we

vmax = 1.0

Mean of environment features

900

vmax = 2.0

vmax = 3.0

vmax = 4.0

describe these environment features as a vector Vt . Also, en-

800

vironment features of a training data set is described as a

700

vector Vp . For each Vp , we calculate the Euclidean distance,

600

dist, between Vt and Vp . During this procedure, the max-

500

imum and the minimum among all the Euclidean distances

400

are recorded as distmax and distmin . Finally, w[i], which is300

a weight for weak classifier i, is obtained by:

200
100

w[i] = 1 −0
AvgReqRec

AvgRepRec

AvgRepSen

AvgDatRec

dist − distmin
distmax − distmin(1)

AvgHelRec

Note that small Euclidean distance has a large weight.
3. 3 Weighting based classification
Figure 2 Diﬀerent vmax to environment features (2)After we calculate weights, we use weights and weak classifiers to obtain final classification results. The result of each

nodes, and number of nodes are respectively set as 700 [m]weak classifier is always probability. In our case, for exam-× 700 [m], 0.2, and 70. We vary vmax from 1.0 [m/s] to 4.0

ple, class of classification is either normal, or malicious. In[m/s].

the case of 0.4 as normal and 0.6 as malicious, the classifi-

Figures 1 and 2 show the diﬀerence of environment features with diﬀerent vmax . From these figures, we can seethat as vmax increases, AvgRerRec, AvgRerSen, AvgNeiMet,

cation result is malicious. We obtain such probabilities fromthe weighting based classification method.
Let w be a weight vector whose element is the weight ofand AvgHelRec increase. As the nodes in the network movema weak classifier. Also, let pn
i and pi be the probabilities

faster, network topology changes more frequently and link(classification result) obtained by a weak classifier i. pn
i isfailures happen more frequently. Consequently, nodes needprobability of normal and pm
i is probability of malicious. We

to send more Rerrs, and meet more neighbors in a window

define the total points of malicious and normal as malsum

time. They hence need to broadcast more hello messages.

and norsum , which are calculated by:

Table 5 illustrates the influence of vmax on rank of behavior features. This table shows that AllPckRatio becomes
more important as vmax becomes larger. AllPckRatio is calculated by (NDatSen + NRepSen) / (NDatRec + NRepRec).

malsum =norsum =∑∑w[i] · pmi(2)

w[i] · pni(3)

As vmax becomes larger, network topology changes more fre-If malsum >= norsum , the result is malicious. Otherwise, it isquently. Malicious nodes thus influence larger part of thenormal.

MANET. AllPckRatio hence increases.

4. Experiment

From this investigation, we can see that environment features and ranks of behavior features are influenced by network environments. In the creation of classifiers, ranks offeatures play an important role. It is hence essential to build

In this section, we conduct experiments of our proposed
method.

4. 1 Setting(iii) False alarm rate: this measures how many instances ob-

We used network simulator Qualnet [18] to obtain the fea-

served from normal nodes are wrongly classified.

tures described in Section 3. 1. 1. In our simulation, the num-

The objective of our method is to detect malicious nodes,

ber of nodes is n. Each mobile node transmits messages andso we consider detecting rate as the most important among

data packets using IEEE 802.11b device. The radio propa-

these 3 criteria.

gation range of each node is adjusted to about 100 meters,

We compare the results in diﬀerent situations.

and the network bandwidth is 11Mbps. Nodes move accord-（ 1 ） Situation 1 (S1): vmax of training set and testinging to the random way point model [6], where the maximum

set are diﬀerent and the other parameters are the same.

move speed is vmax and the pause time is 2 seconds. We（ 2 ） Situation 2 (S2): The area sizes of training set andrandomly choose a pair of source node and destination node

testing set are diﬀerent and the other parameters are theevery 2 seconds. If the source node has an active route tosame.

the destination node, the source node sends a data packet（ 3 ） Situation 3 (S3): The M ratios of training set andto the destination node directly. Otherwise the source node

testing set are diﬀerent and the other parameters are thebroadcasts an RReq to find a route to a destination node.

same.

Note that attack models are categorized into two patterns:（ 4 ） Situation 4 (S4): The node numbers of training set

passive and proactive. Passive attacks include black hole at-and testing set are diﬀerent and the other parameters are thetack, grey hole attack, sybil attack, routing packet droppingsame.

attack, rushing attack, wormhole attack, and jelly fish at-（ 5 ） Situation 5 (S5): All the parameters in training set

tack. Proactive attacks include RReq flooding attack andand testing set are totally diﬀerent.

hello flooding attack. Malicious nodes execute a random（ 6 ） Situation 6 (S6): From Table 6, we get data frompassive attack when they receive messages. Malicious nodes

576 network parameter setting. We randomly choose 115

execute RReq or hello flooding attack for 30 seconds andsetting for testing sets, and the other 461 setting to obtainstop the attack for 30 seconds after the attack. (So, after

training sets.

the stop, they re-start one of the two attacks again.) During

We choose these situations because we want to find influ-

the attack, they broadcast an RReq or a hello packet every

ence of each network parameter on our classifier, and we

0.5 second.

also want to test the robustness of our ensemble classifier

Training and testing data are obtained from the simula-

based on diﬀerent or unknown network environments. Wetion executed by varying the parameters illustrated in Table

compared our weighting mechanism with a naive method, in

6. M ratio is the ratio of malicious nodes over n nodes. The

which the weight of all weak classifiers are set as 1.

time of each simulation is 300 seconds.
Table 6 Parameter configuration

Figure 3 shows the result of accuracy of the naive method
and the weighting mechanism. From this figure, we can seethat the naive method cannot perform well, and the accu-

Parameters

Valuesvmax [m/s]1.0, 2.0, 3.0, 4.0

racy is lower than 80% in each situation. Accuracy of the

500 × 500, 600 × 600, 700 × 700,

weighting mechanism, on the contrary, is very stable, and800 × 800, 900 × 900, 1000 × 1000

always higher than 80%, outperforming the naive method.

Area size [m] × [m]M ratio0.1, 0.2, 0.3, 0.4

n50, 60, 70, 80, 90, 100

Figure 4 shows the result of false alarm rate. From thisfigure, false alarm rate of the weighting mechanism is lower
than the naive method, and it is also very stable, always

As we employed random forest as the classifier, so welower than 20%. In particular, even in S5 and S6, when

tuned the parameters of them properly based on our simu-

the network parameters are random or totally diﬀerent, thelations. The parameters of random forest were decided, i.e.,weighting mechanism can keep low false alarm rate, whilebatchSizePercent, batchSize, NumIterations, and numDeci-the false alarm rate of the naive method is high.

malPlaces are 100, 100, 100, and 2, respectively.

Figure 5 shows the result of detecting rate. This figure

4. 2 Result

illustrates that our weighting mechanism can keep a stable

We evaluate the performance of our proposed methods on

detecting rate in S1, S2, S3, S4, and S5. Detecting rate ofseveral criteria.

the weighting mechanism is better than that of the naive(i) Accuracy: this measures the rate of how many instances

method in all the situations.

are correctly classified among all the instances.

Finally, we test diﬀerent classifier building methods. We(ii) Detecting rate: this measures how many instances ob-

employ SVM, J48 tree and Naive Bayes. We only test themserved from malicious nodes are correctly classified.

in S5. Figure 6 shows the result. From this figure, the ran-

Weighting mechanism

SVM

Naive
100

90

90

80

80

70

70

60

60

50

50

40

40

30

30

20

20

10

10

Accuracy

100

Random forest

J48 tree

Naive Bayes

0

0
S1

S2

S3

S4

S5

S6

Figure 3 Accuracy

Accuracy

Detecting rate

False alarm rate

Figure 6 Comparison of SVM, Random forest, J48 tree, andNaive Bayes

Weighting mechanism

Naive

100

MANETs for secure and proper communications. We pro-90

posed useful features for weak classifiers which judge neigh-80

bor nodes as malicious or normal nodes. For extracting use-

False alarm rate

70
60

ful features, we classified popular attacks in MANETs into

50

four categories. We designed two sets of features: behav-

40

ior features for classifying neighbors and environment fea-

30
20tures for estimating network environments. We used under-

10

sampling to deal with imbalanced data in data set obtained

0

S1

S2

S3

S4

S5

S6

from simulations. We then used weights and weak classifiers
to achieve final result. A series of experiments were conducted to measure the performances of our proposed method.

Figure 4 False alarm rate

The simulation results show that our method can identify
malicious nodes in MANETs eﬀectively.
Weighting mechanism

Naive

As a part of our future work, we plan to create a group de-100
90

cision method for isolating malicious nodes, after they have80

been identified. Because only identifying malicious nodes is

Detecting rate70

not enough, we have to consider a method to block them,60

making it no longer execute attacks.

50
40
30

20
10

References

0
S1

S2

S3

S4

S5

S6

Figure 5 Comparison of detecting rate

dom forest outperforms the other classifiers. Note that J48
tree also performs well, proving that decision trees are eﬀective for our proposed features.

5. Conclusion and future work
This paper addressed the problem of identifying malicious nodes in MANETs with diﬀerent network parametersand multiple attacks.

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