An intrusion detection system using Exponential Henry Gas Solubility Optimization based Deep Neuro Fuzzy Network in MANET

Abstract

The smoothness and dynamic nature of the exploitation are significantly influenced by Mobile Ad hoc Networks (MANETs). The wireless and active environments, on the other hand, are vulnerable to a range of threats. Enhancing the reliable intrusion detection model is crucial to safeguarding the MANET from various malicious attacks. In this paper, an effective intrusion detection method for MANET is presented based on a Deep Neuro Fuzzy Network based on Exponential-Henry Gas Solubility Optimization (EHGSO). The newly designed EHGSO algorithm is used to select the optimal routes in the early phases of safe routing. The fitness metrics that define this approach include energy, distance, neighbourhood quality, and link quality. The proposed EHGSO combines the Henry Gas Solubility Optimization (HGSO) with Exponential Weighted Moving Average (EWMA). The second phase, in which the conveyed data packets are altered and the Knowledge discovery in databases (KDD) features are extracted, starts the intrusion detection phase at the base station. Following the extraction of the KDD features, data augmentation is performed. The Deep Neuro Fuzzy Network is trained using the suggested EHGSO method before performing intrusion detection. The proposed method demonstrates higher performance when compared to all other existing technologies. In without attacks scenario, the values achieved by the proposed method considering the metrics, such as energy, throughput, packet drop, jitter, Performance and Development Review (PDR), precision, and recall is 0.342 J, 134975 kbps, 4.123, 0.086, 95.877%, 0.950, and 0.924, respectively.