Hybrid elk herd green anaconda-based multipath routing and deep learning-based intrusion detection In MANET

Abstract

A Mobile Ad-Hoc Network (MANET) represents a set of wireless networks that create the network without requiring centralized control. Moreover, the MANET serves as an effectual communication network but is impacted by security issues. MANET intrusion detection constantly monitors network traffic for potential intrusions. Still, it requires network nodes for analyzing, and processing the data, which leads to the highest processing charge. For solving such difficulties, the EIK Herd Anaconda Optimization (EHAO)-based routing, and EHAO-trained Deep Kronecker Network (EHAO-DKN) for intrusion detection is devised in this paper. The MANET simulation is the prime step for attaining the routing. The proposed EHGAO with the fitness factors are considered in the routing. The intrusion presence in the MANET is detected at the Base Station (BS), where the Z-score normalization is applied to normalize the log data. The Wave Hedges metric effectively selects the relevant features, and the EHAO-DKN detects the intrusion. Furthermore, the EHAO-based routing obtained the optimal trust, energy, and delay of 85.30, 2.905 J, and 0.608 mS as well as the accuracy, sensitivity, and specificity of 92.40 %, 91.50 %, and 91.50 % are achieved by the EHAO-DKN-based intrusion detection.