Sinkhole Attack Detection-Based SVM In Wireless Sensor Networks

Sihem Aissaoui and Sofiane Boukli Hacene
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Sihem Aissaoui: EEDIS Laboratory, Computer Science Departement, Djilali Liabes University of Sidi Bel Abbes, Algeria
Sofiane Boukli Hacene: EEDIS Laboratory, Computer Science Departement, Djilali Liabes University of Sidi Bel Abbes, Algeria

International Journal of Wireless Networks and Broadband Technologies (IJWNBT), 2021, vol. 10, issue 2, 16-31

Abstract: Wireless sensor network is a special kind of ad hoc network characterized by high density, low mobility, and the use of a shared wireless medium. This last feature makes the network deployment easy; however, it is prone to various types of attacks such as sinkhole attack, sybil attack. Many researchers studied the effect of such attacks on the network performance and their detection. Classification techniques are some of the most used end effective methods to detect attacks in WSN. In this paper, the authors focus on sinkhole attack, which is one of the most destructive attacks in WSNs. The authors propose an intrusion detection system for sinkhole attack using support vector machines (SVM) on AODV routing protocol. In the different experiments, a special sinkhole dataset is used, and a comparison with previous techniques is done on the basis of detection accuracy. The results show the efficiency of the proposed approach.

Date: 2021
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