A Brief Study on Different Intrusions and Machine Learning-Based Anomaly Detection Methods in Wireless Sensor Networks

Wireless Sensor Networks (WSN) consist of a number of resource constrained sensors to collect and monitor data from unattended environments. Hence, security is a crucial task as the nodes are not provided with tamper-resistance hardware. Provision for secured communication in WSN is a challenging task especially due to the environment in which they are deployed. One of the main challenges is detection of intrusions. Intrusion detection system gathers and analyzes information from various areas within a computer or a network to identify possible security breaches. Different intrusion detection methods have been proposed in the literature to identify attacks in the network. Out of these detection methods, machine-learning based methods are observed to be efficient in terms of detection accuracy and alert generations for the system to act immediately. A brief study on different intrusions along with the machine learning based anomaly detection methods are reviewed in this work. The study also classifies the machine learning algorithms into supervised, unsupervised and semi-supervised learning-based anomaly detection. The performances of the algorithms are compared and efficient methods are identified.