Deep Learning and Machine Learning Techniques for Intrusion Detection and Prevention in Wireless Sensor Networks: Comparative Study and Performance Analysis

Abstract

Nowadays, there is remarkable growth in technology and wireless sensor networks. These are primarily used for the purpose of communication. Communication between devices may be wired or wireless, hence, the chance of attacks through the networks is increasing daily. For secure communication, intrusion detection and prevention are primary concerns. Thus, analyses of intrusion detection and prevention techniques have become an important part of the engineering field. With the assistance of intrusion detection and prevention system, we are able to determine and then notify the normal and abnormal activities of the users. Thus, there’s a requirement to design effective intrusion detection and prevention system by exploitation machine learning and deep learning for wireless sensor networks. In this work, a comparative study and performance analysis of different machine learning and deep learning techniques are given for intrusion detection and prevention system. The performance evaluation of these techniques is done by experiments conducted on WSN-DS dataset. The comparative analysis shows that deep learning classifiers shows better intrusion detection results than machine learning techniques. In this work, Convolutional Neural Network classifier is used.