Hybrid Optimization Algorithm_Dense ResNeXt Fused Deep Stacked Autoencoder for Wormhole Attack Mitigation on Network Control System

Abstract

Wireless sensor network (WSN) is an advanced technology in the current scenario owing to its broad range of research. Due to constraints such as restricted bandwidth and ever-changing network structures, WSNs are inherently exposed to a wide range of security vulnerabilities. This inherent fragility has sparked a significant surge in research efforts focused on enhancing the security mechanisms of WSNs in recent years. Thus, the research on WSN security has been growing for the past few years. In terms of security, the less infrastructure and self-reliant nature of WSN is considered a difficult concern. A wormhole (WH) attack detection system on Networked Control Systems (NCSs) is developed to conquer this issue by employing the Secretary Pufferfish Optimization Algorithm enabled Dense ResNeXt fused Deep Stacked Autoencoder (SPOA_DResNeXt-DSAE). Firstly, WSN simulation is performed and routing is executed using Low Energy Adaptive Clustering Hierarchy (LEACH). In order to perform WH attack detection, three processes, like Neighbour Ratio Threshold (NRT), out-of-band and in-band WH detection, are conducted. In the final phase, detection of the WH attack is effectively carried out through the application of the ResNeXt-DSAE framework. Additionally, the attack mitigation is done by means of DResNeXt-DSAE, which is trained using the Secretary-Pufferfish Optimisation Algorithm (SPOA). The effectiveness of DResNeXt-DSAE is evaluated using throughput, delay and Packet Delivery Ratio (PDR), which observed better values of 0.570 sec, 0.704 Mbps and 0.882.

Keywords

Wormhole attack / attack detection / attack mitigation / routing / wireless sensor networks

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Systems Engineering Society of China and Springer-Verlag GmbH Germany

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