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Mobile Ad-hoc Network (MANET) is a distributed, decentralized network of wireless portable nodes connecting directly without any fixed communication base station or centralized administration. Nodes in MANET move continuously in random directions and follow an arbitrary manner, which presents numerous challenges to these networks and make them more susceptible to different security threats. Due to this decentralized nature of their overall architecture, combined with the limitation of hardware resources, those infrastructure-less networks are more susceptible to different security attacks such as black hole attack, network partition, node selfishness, and Denial of Service (DoS) attacks. This work aims to present, investigate, and design an intrusion detection predictive technique for Mobile Ad hoc networks using deep learning artificial neural networks (ANNs). A simulation-based evaluation and a deep ANNs modelling for detecting and isolating a Denial of Service (DoS) attack are presented to improve the overall security level of Mobile ad hoc networks.

No abstract

Mobile ad hoc network (MANET) is an infrastructure-less and decentralized network without any physical connections. Nodes are mobile, free to move, and independent of each other which makes routing a difficult task. Hence, a dynamic routing protocol is needed to make MANET reliable and function properly. Several routing protocols have been proposed with different working mechanisms and performance levels. Therefore, the performance study of those protocols is needed. This paper evaluates the performance of MANET routing protocols using simulation based experiments to observe the behavior of the network as the density of the nodes increases. The paper evaluates the performance of proactive (fisheye state routing), reactive (ad hoc on-demand distance vector), and hybrid (zone routing protocol) routing protocols in terms of the packet delivery fraction, average throughput, and average end-to-end delay. The simulations of protocols to analyze their performance in different conditions were performed using the network simulator 2 (NS 2).

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