A Novel Fuzzy Anomaly Detection Algorithm Based on Hybrid PSO-Kmeans in Content-Centric Networking

Abstract

In Content-Centric Networks (CCNs) as a promising network architecture, new kinds of anomalies will arise. Usually, clustering algorithms would fit the requirements for building a good anomaly detection system. K-means is a popular anomaly detection method; however, it suffers from the local convergence and sensitivity to selection of the cluster centroids. This chapter presents a novel fuzzy anomaly detection method that works in two phases. In the first phase, authors propose an hybridization of Particle Swarm Optimization (PSO) and K-means algorithm with two simultaneous cost functions as well-separated clusters and local optimization to determine the optimal number of clusters. When the optimal placement of clusters centroids and objects are defined, it starts the second phase. In this phase, the authors employ a fuzzy approach by the combination of two distance-based methods as classification and outlier to detect anomalies in new monitoring data. Experimental results demonstrate that the proposed method can yield high accuracy as compared to preexisting algorithms.