Title: Particle swarm optimisation algorithm-based intrusion detection system for mobile ad hoc network

Authors: Shruti Dixit; Rakesh Singhai

Addresses: Department of Electronics and Communication, UIT, RGPV, Bhopal, India ' Department of Electronics and Communication, UIT, RGPV, Bhopal, India

Abstract: Ad hoc network is the category of cooperative wireless mobile nodes with the absence of centralised authority or infrastructure. It is vulnerable to different types of attacks such as passive and active because of open environment, speedily varying topology and decentralised nodes. An attack by a malicious node known as 'black hole attack' can considerably diminishes ad hoc network performance and results in disintegration of network, inaccessible nodes and severely degrades network performance. Evaluation of the additional effect due to existence of malicious nodes and determination of the suitable measures to detect these nodes in the network is the prime objective of the research work. In order to overcome this misbehaviour activity in the network, a particle swarm optimisation algorithm-based intrusion detection system (PSO-IDS) for ad hoc networks is proposed. Swarm agents are effectively used to detect the malicious node by evaluating their packet behaviour based on packet delivery fraction (PDF), average delay, number of packets dropped and packet capture. A defence technique called IDS is deployed for prevention against black hole attack due to malicious nodes. The simulation study establishes that the proposed PSO-IDS algorithm improves the performance metrics like throughput and packet delivery ratio.

Keywords: mobile ad hoc network; MANET; particle swarm optimisation; PSO; intrusion detection systems; IDSs; malicious nodes; black hole attack; AODV; swarm agents; packet delivery ratio; throughput; PSO-IDS; network routing load.

DOI: 10.1504/IJAC.2018.097618

International Journal of Autonomic Computing, 2018 Vol.3 No.2, pp.114 - 129

Received: 04 Apr 2018
Accepted: 19 Aug 2018

Published online: 31 Jan 2019 *

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