Abstract--Large-scale attacks like Distributed Denial-ofService (DDoS) attacks still pose unpredictable threats to the Internet infrastructure and Internet-based business. Thus, ma...
In this work we consider the problem of monitoring information streams for anomalies in a scalable and efficient manner. We study the problem in the context of network streams wher...
Abstract. We present a method that improves the results of network intrusion detection by integration of several anomaly detection algorithms through trust and reputation models. O...
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 ...
Combining an "anomaly" and a "misuse" IDSes offers the advantage of separating the monitored events between normal, intrusive or unqualified classes (ie not kn...