An Intrusion Detection System for Wireless Sensor Networks

Attacks in Wireless Sensor Networks (WSNs) aim in limiting or even eliminating the ability of the network to perform its expected function. WSNs are networks with limited resources and often deployed in uncontrollable environments that an intruder can easily access. WSN attacks target specific network layer's vulnerabilities but normally affect other layers as well. Local sensor activity at multiple sensor network layers should be monitored and evaluated to detect possible malicious intervention. In this work we propose a general methodology of an anomaly-based Intrusion Detection System (IDS), named mIDS, that uses the Binary Logistic Regression (BLR) statistical tool to classify local sensor activity to either benign or malicious. We evaluate the proposed system using routing layer attacks and we show that mIDS is able to detect malicious activity within the range of 88%-100%.