Entropy-Based Anomaly Detection Using Observation Points Relations in Wireless Sensor Networks

Wireless sensor networks play the most important role in the internet of things (IoT). Due to the extensive development of sensor networks, the innate limitations and characteristics of the resources in sensors, and heterogeneity of equipment, wireless sensor network has been confronted with security challenges and various vulnerabilities. One way to improve the reliability of the sensor networks is to use abnormal behaviors detection methods in the network. The current paper presents a detection method for abnormal behaviors in a distributed way using a division technique based on entropy and closeness of cumulative observation points. The proposed algorithm for detecting intrusions processed in an intranetwork manner and separates abnormal data from normal data and then classifies it and reports to the higher levels. To investigate the efficiency and accuracy of the detection, multilayer hierarchical topology and simulations based on MATLAB software were employed. The results show high accuracy of the proposed method in detecting abnormal behavior in different levels of wireless sensor networks.