Evaluation of Feature Selection Techniques in Intrusion Detection Systems Using Machine Learning Models in Wireless Ad Hoc Networks

Nagalakshmi, T.J.; Balasaraswathi, M.; Sivasankaran, V.; Ravikumar, D.; Gladwin, S. Joseph; Kumar, S. Pravin

doi:10.1002/9781119682806.ch3

Cited by 1 publication

(1 citation statement)

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“…Intrusion detection is becoming a vital approach for screening system movements and identifying attacks in network like anomalous networks, unwanted network access, and hostile attack on computer system. A major segment of present arrangements adheres to systems, but they do not react to unexpected conditions [3]. There are two types of IDS: misuse and anomaly-based IDS.…”

Section: Introductionmentioning

confidence: 99%

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

Reji¹,

Joseph²,

Thaiyalnayaki³

et al. 2023

Computer Systems Science and Engineering

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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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Section: Introductionmentioning

confidence: 99%