The Mechanism of Anomaly Detection in Wireless Sensor Network: An Innovative Approach

Abstract

Wireless Sensor Networks (WSNs) have emerged as one of the most important research areas, large numbers of limited resource sensor nodes are used to monitor the physical environment and report any significant information. Many different anomaly detection systems (ADS) have been proposed in the literature over the years. Now apply an algorithm to increase detection sensitivity. Detection of sensor data irregularities is useful for practical applications as well as for network management, because the patterns found can be used for both decision making in applications and system performance tuning. The problem of irregularities detection is to find those sensory values that deviate significantly from the norm. This problem is especially important in the sensor network setting because it can be used to identify abnormal or interesting events or faulty sensors. Dynamic detection model generated using a combination of different data vectors are required to detect time variant anomalies in WSNs. Decentralized, Individual nodes should perform the anomaly detection independently in the local environment. The scope of this thesis is to develop and make the ADS scalable and robust against attacks. The communication cost can be reduced if only abnormal sensory values, as opposed to all values, need to be transmitted. It is essential to mine the sensor readings for patterns in real time in order to make intelligent decisions promptly. General Terms Wireless sensor network, Anomaly Detection, Security, Algorithms.