Intrusion Detection System using One Class SVM with and without Feature Selection in Wormhole Attack Detection
T. J. Nagalakshmi1, P. C. Kishore Raja2, S. Pravin Kumar3, V. Veeramanikandan4

1T. J. Nagalakshmi, Assistant Professor, Department of ECE, Saveetha School of Engineering, SIMATS, Chennai (Tamil Nadu), India.

2Dr. P. C. Kishore Raja, Professor, Department of ECE, SRM University, Delhi – NCR, Sonepat (Haryana), India.

3S. Pravin Kumar, UG Student, Department of ECE, Saveetha School of Engineering, SIMATS, Chennai (Tamil Nadu), India.

4V. Veeramanikandan, UG Student, Department of ECE, Saveetha School of Engineering, SIMATS, Chennai (Tamil Nadu), India.

Manuscript received on 14 November 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 629-638 | Volume-9 Issue-2S4 December 2019 | Retrieval Number: B12301292S419/2019©BEIESP | DOI: 10.35940/ijitee.B1230.1292S419

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Abstract: An Ad-hoc network is a kind of wireless construction from one to another computer, without having Wi-Fi access point or Router. However, the Ad hoc approach offers marginal security and decreases the data transfer rate. Consequently, it helps the attacker to connect with the ad-hoc network without any trouble. Therefore, a robust and reliable intrusion detection system (IDS) is a necessity of today’s information security domain. These IDS systems play a vital role in monitoring the threats encountered in a network by detecting the change in the normal profile due to attacks. Recently, to detect attacks the IDS are being equipped with machine learning algorithms to attain better accuracy and fast detection speed. Most of the IDS use different network features. However, enormous number of features makes the detection and prevention complicated. The IDS presented in this paper employs random forest and principal component analysis to minimize the number of features for network IDS for wireless ad hoc networks. The one class SVM has been used for detection of worm hole attack with and without feature selection. The performances of these approaches are compared with various existing techniques with false positive rate (FPR), accuracy and detection rate. Here, the accuracy improves and false positive rate reduces when intrusion is detected with feature selection technique. This paper discusses the performance of the one class SVM classifier in the wireless adhoc network IDS with random forest feature selection and principal component analysis feature selection techniques and one class SVM classifier without feature selection technique in the detection of wormhole attack. And the performance of one class SVM IDS is better in the detection of wormhole attack while it is implemented with principal component analysis feature selection technique.

Keywords: Wireless adhoc Network, Intrusion Detection System, Feature Selection by Random Forest Method and Principal Component Analysis, One Class SVM, Performance Metrics of IDS.
Scope of the Article: Adaptive Systems