A Heterogeneous Framework to Detect Intruder Attacks in Wireless Sensor Networks

Abstract

Abstract-Wireless sensor network (WSN) has been broadly implemented in real world applications, such as monitoring of forest fire, military targets detection, medical and/or science areas and above all in our daily home life as well. Nevertheless, WSNs are effortlessly compromised by adversaries due to their broadcast transmission medium as a means of communication which are lacking in tamper resistance. Consequently, an intruder can over hear all traffic, replay previous messages, inject malicious data packets, or can compromise a node. Commonly, sensor nodes are very much vulnerable of two main issues in security aspect that are node authentication and compromising a node. In this paper, a heterogeneous framework of node capture and intrusion detection for WSNs is proposed. This framework efficiently detects the captured nodes by using a novel technique, embedded with an Intrusion Detection mechanism which aggregates Signature and Anomaly based approach with Neural Network Multi-Layer Perceptron (MLP) classification in a clustering environment. Moreover, the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a security shield to real WSN applications.