Intrusion detection is a critical component of secure information systems. Network anomaly detection has been an active and difficult research topic in the field of Intrusion Dete...
This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and ...
This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the n...
In this paper the node-level decision unit of a self-learning anomaly detection mechanism for office monitoring with wireless sensor nodes is presented. The node-level decision uni...
Abstract. Intrusion detection has been extensively studied in the last two decades. However, most existing intrusion detection techniques detect limited number of attack types and ...