Anomaly detection in wireless sensor networks

Abstract

Wireless Sensor Network can be defined as a network of integrated sensors responsible for environmental sensing, data processing and communication with other sensors and the base station while consuming low power. Today, WSNs are being used in almost every part of life. The cost effective nature of WSNs is beneficial for environmental monitoring, production facilities and security monitoring. At the same time WSNs are vulnerable to security breaches, attacks and information leakage. Anomaly detection techniques are used to detect such activities over the network that do not conform to the normal behavior of the network communication. Supervised Machine learning approach is one way to detect anomalies where a normal model is developed with known responses called labels and this model is tested against new data sets. We experimented Supervised Machine Learning approach for the labelled sensor data set of Humidity and Temperature and the results show that KNN (K Nearest Neighbor) proves to be the best anomaly detection algorithm for this data set.