Multi head self-attention gated graph convolutional network based multi‑attack intrusion detection in MANET

Designing of intrusion detection system (IDS), and mobile ad hoc networks (MANET) prevention technique with examined detection rate, memory consumption with minimum overhead are vital concerns. Node mobility and node energy are the two optimization problems in MANETs wherein nodes travel uncertainly in any direction, evolving in a continuous change of topology. With the proposed approach, a Centrality Coati Optimization Algorithm based Cluster Gradient for multi attack intrusion identification is devised. This study focuses on the problems of node mobility and energy to develop a clustering algorithm for cluster head selection in MANET that is incited by Dual Network Centrality. Compact cluster formation is carried out by Coati Optimization Algorithm (COA). The Multi-head Self-Attention based Gated Graph Convolutional Network (MSA-GCNN) with a hybrid type of IDS recognizes several attacks, including DoS and Zero-Day attacks. The proposed technique is implemented in NS-2 network simulator. The performance of proposed approach is examined under some parameters, like attack detection rate, memory consumption, computational time for detecting the intruder. The outcomes display that the proposed technique decreases the IDS traffic and entire consumption of memory and sustains a higher attack identification rate with less computational time. The proposed technique attains 4.299 %, 10.375 % and 6.935 % Accuracy, 5.262 %, 8.375 % and 7.945 % Precision, 7.282 %, 10.365 % and 5.935 % Recall, 9.272 %, 5.355 % and 8.965 % ROC is higher compared with the existing methods such as, Epsilon Swarm Optimized Cluster Gradient along deep belief classifier for multiple attack intrusion detection (ESOC-MA-ID-MANET), Intrusion Detection secure solution for intrusion detection in cloud computing utilizing hybrid deep learning approach called EOS-IDS and improved heap optimization (IHO-MA-ID-MANET) for induction detection technique respectively.