Detecting Energy Depriving Malicious Nodes by Unsupervised Learning in Energy Harvesting Cooperative Wireless Sensor Networks

Abstract

This paper presents an unsupervised learning-based method for detecting energy depriving malicious nodes in an energy harvesting cooperative wireless sensor network (EHC-WSN). In EHC-WSNs, nodes wirelessly transfer a portion of their energy to their neighboring nodes if their neighboring nodes lack energy. A malicious energy depriving mode may falsibly show that it has little energy and in this way obtains energy from neighboring nodes, thus depriving them of energy. To detect these malicious nodes, we utilize a clustering method. In our method, each node first observes the energy of its neighboring nodes, then it utilizes this information to obtain data points for the clustering. After clusters are formed, each node judges on a cluster of data points from malicious nodes and makes a malicious node determination. We investigate the performance of our method and confirm that our method outperforms the baseline method in terms of detection accuracy and false detection rate.