Developed Density Peak Clustering With Support Vector Data Description for Access Network Intrusion Detection

Abstract

Access network intrusion detection (ANID) is an indispensable role in guaranteeing information security, in which data-classification is a key operating procedure. In this paper, we propose an algorithm of the developed density peak clustering with support vector data description (SVDD), and it can break through the bottleneck of processing high-dimensional data with non-uniform density in the ANID system. The cutoff distance $(dc)$ depending on experience in density peak clustering is then optimized by our defined coefficient called adjusted silhouette coefficient (ASIL). This can overcome the unfitness of traditional SIL due to multi-noise samples and clustering instability. In order to generate an accurate classifier, an improved particle swarm optimization algorithm is also developed to the parameters of SVDD. Finally, the proposed algorithm is evaluated through UCI standard data sets and KDD 99 data sets, and these results show higher accuracy and robustness compared with the conventional methods.