Detection of intrusions and malware & vulnerability assessment, GI SIG SIDAR workshop, DIMVA 2004 Laskov, Pavel; Christin, Schäfer; Kotenko, Igor
Practical application of data mining and machine learning techniques to intrusion detection is often hindered by the difficulty to produce clean data for the training. To address this problem a geometric framework for unsupervised anomaly detection has been recently proposed. In this framework, the data is mapped into a ...
