# EBR: Routing Protocol to Detect Blackhole Attacks in Mobile Ad Hoc Networks
## 1 Introduction  
A MANET is a self-configuring network of dynamic nodes connected through wireless links. Every node may act as being a router or host, and it can move freely in any direction [1]. Data communication is achieved by routing packets to the end nodes through intermediate nodes. Numerous routing protocols are designed for communication in MANETs, and they are classified into three major types: proactive, reactive, and hybrid protocols [2]. Ad hoc On-demand Multipath Distance Vector (AOMDV) is a reactive routing protocol [3]. AOMDV is an efficient protocol when compared with single- route mechanisms such as Ad-hoc On-demand Distance Vector (AODV), particularly in case of nodes mobility where the amount of dropped data augments. On the other hand, MANETs are susceptible to security issues and attacks because of the ease of mobility and its nature of having no infrastructure. A blackhole attack is also known as a packet-dropping attack, and this greatly reduces the network performance. Mostly we have two types of blackhole attacks, which are single blackhole node or multiple blackhole nodes. When there are several blackhole nodes, they collaborate with one another to disturb communication, and this is called a cooperative blackhole attack [4; 5; 6; 7]. In normal reactive routing protocols and when a source node is willing to send data to a destination node and does not have a path, it sends out route request packets (RREQ). The data packets will be forwarded to the destination upon receiving route- reply packets (RREP) from the intermediate nodes or the destination. Nonetheless, the blackhole node, when receiving an RREQ packet, sends back an RREP promptly with false information of having the shortest path to the receiving end with a quite large sequence number. This high sequence number indicates the freshness of the path. If the source node picks such a route having blackhole node(s), these malicious nodes will drop the data packet [8]. There are several solutions proposed to prevent the blackhole attacks. Security measures were added to existing protocols and cryptographic mechanisms were added to the packets [9; 10]. MANETs have limited resources; however, most of these methods impose burdens such as delay and excessive energy consumption, which in turn would diminish the lifetime of the network. On the other hand, different trust schemes and confidence models have been proposed [11; 12]. This is to enhance the security in MANETs where nodes can ensure a neighbor's confidence directly or through recommendations from other nodes in the network. There are several security methods, such as in [13; 14; 15; 16]. Cryptographic mechanisms consume a lot of energy for computation; however, these methods may behave worse than those non-secured techniques when some attackers exist [17; 18]. Providing a securing protection to the routing messages does not necessarily assure the ability to detect the suspicious nodes [19]. This motivated us to develop a mechanism that highly secures data packets, reduces the energy consumption, and enhances the quality of service (QoS) performance. In this paper, we propose an Enhanced Blackhole Resistance (EBR) algorithm to detect and, therefore, prevent or at least mitigate the data packets from going through routes having malevolent nodes in mobile networks. EBR can be applied to any reactive protocol. To select the path that enhances the data delivery performance, EBR is better to be used with a multipath routing protocol, so we use it here with an AOMDV method to obtain optimized routes. Each node inspects its neighbors and gives them confidence values to identify the blackhole nodes. This algorithm provides a solution for both single and collaborative attacks, and it significantly shows excellent performance when compared with other protocols. Each node can determine the confidence level of neighboring nodes through employing a combination of time to live (TTL) and round trip time (RTT), and this in turn can determine the existence of the blackhole node(s).