Epilson Swarm Optimized Cluster Gradient and deep belief classifier for multi-attack intrusion detection in MANET

Abstract

Design of intrusion detection, and MANET prevention mechanism, with scrutinized detection rate, memory consumption with minimal overhead are crucial research concerns. Node mobility and energy of the node are dual essential optimization issues in mobile ad hoc networks (MANETs) where nodes traverse uncertainly in any direction, evolving in topology's continuing modification. A Centrality Epilson Greedy Swarm and Gradient Deep Belief Classifier (CEGS-GDBC) for multi-attack intrusion detection are designed with the proposed method. The paper concentrates on the issues of node mobility and energy to emerge a clustering algorithm inspired by Dual Network Centrality for cluster head election in MANET. Compact cluster formation is done with the help of Epilson Greedy Swarm Optimization. Finally, with a hybrid type of IDS, Gradient using the Deep Belief Network Classifier identifies multi-attack, i.e., DoS and Zero-Day attack. The proposed work is experimented extensively in the NS-2 network simulator and compared with the other existing algorithms. The proposed method's performance is studied in terms of different parameters such as attack detection rate, memory consumption, and computational time for identifying and isolating the intruder. Simulation results show that the proposed method extensively minimizes the IDS traffic and overall memory consumption and maintains a high attack detection rate with minimal computational time. From the results, CEGS-GDBC method increases the attack detection rate by 31% and reduces the memory consumption and computational time by 39% and 41% as compared to Fuzzy elephant—Herd optimization and Cross centric intrusion detection system.