Centered Hyperspherical and Hyperellipsoidal One-Class Support Vector Machines for Anomaly Detection in Sensor Networks

Abstract

Anomaly detection in wireless sensor networks is an important challenge for tasks such as intrusion detection and monitoring applications. This paper proposes two approaches to detecting anomalies from measurements from sensor networks. The first approach is a linear programming-based hyperellipsoidal formulation, which is called a centered hyperellipsoidal support vector machine (CESVM). While this CESVM approach has advantages in terms of its flexibility in the selection of parameters and the computational complexity, it has limited scope for distributed implementation in sensor networks. In our second approach, we propose a distributed anomaly detection algorithm for sensor networks using..