Open Access

ARTICLE

Genetic-based Fuzzy IDS for Feature Set Reduction and Worm Hole Attack Detection

by M. Reji^1,*, Christeena Joseph², K. Thaiyalnayaki², R. Lathamanju²

1 Department of Electronics and Communication Engineering, Rohini College of Engineering and Technology, Palkulam, Kanyakumari, India
2 Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India

* Corresponding Author: M. Reji. Email:

Computer Systems Science and Engineering 2023, 45(2), 1265-1278. https://doi.org/10.32604/csse.2023.026776

Received 04 January 2022; Accepted 23 March 2022; Issue published 03 November 2022

Abstract

The wireless ad-hoc networks are decentralized networks with a dynamic topology that allows for end-to-end communications via multi-hop routing operations with several nodes collaborating themselves, when the destination and source nodes are not in range of coverage. Because of its wireless type, it has lot of security concerns than an infrastructure networks. Wormhole attacks are one of the most serious security vulnerabilities in the network layers. It is simple to launch, even if there is no prior network experience. Signatures are the sole thing that preventive measures rely on. Intrusion detection systems (IDS) and other reactive measures detect all types of threats. The majority of IDS employ features from various network layers. One issue is calculating a huge layered features set from an ad-hoc network. This research implements genetic algorithm (GA)-based feature reduction intrusion detection approaches to minimize the quantity of wireless feature sets required to identify worm hole attacks. For attack detection, the reduced feature set was put to a fuzzy logic system (FLS). The performance of proposed model was compared with principal component analysis (PCA) and statistical parametric mapping (SPM). Network performance analysis like delay, packet dropping ratio, normalized overhead, packet delivery ratio, average energy consumption, throughput, and control overhead are evaluated and the IDS performance parameters like detection ratio, accuracy, and false alarm rate are evaluated for validation of the proposed model. The proposed model achieves 95.5% in detection ratio with 96.8% accuracy and produces very less false alarm rate (FAR) of 14% when compared with existing techniques.

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Keywords

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