Journal Article

Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks

Safaldin, Mukaram, Abualigah, Laith, Otair, Mohammed

2021

Overview

Intrusion in wireless sensor networks (WSNs) aims to degrade or even eliminating the capability of these networks to provide its functions. In this paper, an enhanced intrusion detection system (IDS) is proposed by using the modified binary grey wolf optimizer with support vector machine (GWOSVM-IDS). The GWOSVM-IDS used 3 wolves, 5 wolves and 7 wolves to find the best number of wolves. The proposed method aims to increase intrusion detection accuracy and detection rate and reduce processing time in the WSN environment through decrease false alarms rates, and the number of features resulted from the IDSs in the WSN environment. Indeed, the NSL KDD’99 dataset is used to demonstrate the performance of the proposed method and compare it with other existing methods. The proposed methods are evaluated in terms of accuracy, the number of features, execution time, false alarm rate, and detection rate. The results showed that the proposed GWOSVM-IDS with seven wolves overwhelms the other proposed and comparative algorithms.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Accuracy / Algorithms / Artificial Intelligence / Computational Intelligence / Data encryption / Data integrity / Datasets / Engineering / False alarms / Feature selection / Intrusion detection systems / Optimization / Original Research / Robotics and Automation / Sensors / Support vector machines / User Interfaces and Human Computer Interaction / Wireless sensor networks / Wolves