ABSTRACT
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.
Keywords: - Anomaly Detection, Intrusions, Intrusion Detection System, Machine-learning algorithms