Enhanced SVM-Based Novel Detection of Intrusions for Wireless Sensor Networks (WSNS)
Keywords:
Wireless sensor network (WSN), intrusion detection (ID), DoS attacks, enhanced support vector machine (ESVM), chaotic levy grasshopper optimization (CLGO)Abstract
The use of wireless sensor networks (WSNs) is expanding rapidly due to the quick advancement of wireless sensor technology. WSNs have significant military significance as well as a wide range of potential commercial applications. However, it has significant security issues because of factors such as the lack of resources available for terminal equipment and the nature of the wireless communication environment. Wireless sensor networks (WSNs) are susceptible to a variety of assaults because of their dispersed architecture and limited resources, making intrusion detection for WSNs an essential part of network security. The purpose of intrusion detection systems (IDS) in WSNs is to detect and react to intrusion attempts and other harmful activity. It takes a long time to perform a conventional intrusion detection algorithm. As a result, we have developed a novel intrusion detection framework for the wireless sensor network to help prevent this issue. This paper's major contribution is the proposal of an enhanced support vector machine (ESVM)-based intrusion detection algorithm and the construction of an intrusion detection system (IDS) for WSN's DoS attacks. Additionally, the suggested method's performance is enhanced by chaotic levy grasshopper optimization (CLGO). From the perspectives of detection rate, packet delivery rate, transmission delay, and energy consumption analysis, the proposed IDS can significantly improve network performance by identifying and removing malicious nodes in the network. It has the features of a simple structure, a short computation time, and a high detection rate.
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