Ad hoc networks, known as infrastructure-less networks, are composed of mobile nodes that connect without a centralized system controlling them. These networks have a wide range of potential applications, including emergency response, events, military operations, wireless access, and intelligent transportation. They can take on various forms, such as wireless sensor networks, wireless mesh networks, and mobile ad hoc networks. Because users in these networks can move around at any time, routing protocols must adapt to the constantly changing network layout. However, these networks are also susceptible to various security threats, including DDoS attacks. This paper aims to analyze the performance and impact of security attacks on the performance of reactive and proactive routing protocols in CBR connection patterns with different pause times. The analysis is provided in metrics such as throughput, packet loss, end-to-end delay, and load. The simulation results show that, on average, the OPNET Modeler simulator analyzed the performance results under DDoS attacks under voice and video traffic conditions. Furthermore, the paper explores the use of Honeypot intelligent agents as a solution to increase security by creating a dummy node to fool DDoS attackers. The results show that the OLSR protocol is most affected by DDoS attacks in terms of quality-of-service metrics such as packet loss, throughput, end-to-end delay, and load. The number of responses to the honeypot solutions differs for each protocol.

An important issue in ad-hoc on-demand distance vector (AODV) routing protocols is route failure caused by node mobility in the MANETs. The AODV requires a new route discovery procedure whenever a route breaks and these frequent route discoveries increase transmission delays and routing overhead. The present study proposes a new method for AODVs using a genetic algorithm to improve the route recovery mechanism. When failure occurs in a route, the proposed method (GAAODV) makes decisions regarding the QOS parameter to select source or local repair. The task of the genetic algorithm is to find an appropriate combination of weights to optimize end-to-end delay. This paper evaluates the metrics of routing overhead, average end-to-end delay, and packet delivery ratio. Comparison of the new algorithm and AODV (RFC 3561) using a NS-2 simulator shows that GAAODV obtains better results for the QOS parameters.

In mobile ad hoc network (MANET), secure communication is more challenging task due to its fundamental characteristics like having less infrastructure, wireless link, distributed cooperation, dynamic topology, lack of association, resource constrained and physical vulnerability of node. In MANET, attacks can be broadly classified in two categories: routing attacks and data forwarding attacks. Any action not following rules of routing protocols belongs to routing attacks. The main objective of routing attacks is to disrupt normal functioning of network by advertising false routing updates. On the other hand, data forwarding attacks include actions such as modification or dropping data packet, which does not disrupt routing protocol. In this paper, we address the “Packet Drop Attack”, which is a serious threat to operational mobile ad hoc networks. The consequence of not forwarding other packets or dropping other packets prevents any kind of communication to be established in the network. Therefore, there is a need to address the packet dropping event takes higher priority for the mobile ad hoc networks to emerge and to operate, successfully. In this paper, we propose a method to secure ad hoc on-demand distance vector (AODV) routing protocol. The proposed method provides security for routing packets where the malicious node acts as a black-hole and drops packets. In this method, the collaboration of a group of nodes is used to make accurate decisions. Validating received RREPs allows the source to select trusted path to its destination. The simulation results show that the proposed mechanism is able to detect any number of attackers.