Distributed Segment-Based Segment Based Anomaly Detection With Kullback Kullback–Leibler Divergence in Wireless Sensor Networks
Abstract: In this paper, we focus on detecting a special type of anomaly in wireless sensor network (WSN), which appears simultaneously in a collection of neighboring nodes and lasts for a significant period of time. Existing point-based point based techniques, in this context, are not very effective and efficient. With the proposed distributed segment-based based recursive kernel density estimation, a global probability density function can be tracked and its difference between every two periods of time is continuously measured for decision d making. Kullback-Leibler Leibler (KL) divergence is employed as the measure and, in order to implement distributed in-network in estimation at a lower communication cost, several types of approximated KL divergence are proposed. In the meantime, an entropic graph graph-based algorithm that operates in the manner of centralized computing is realized, in comparison with the proposed KL divergence-based divergence based algorithms. Finally, the algorithms are evaluated using a real-world world data set, which demonstrates that they are able to achieve a comparable performance at a much lower communication cost.