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Intrusion detection in unlabeled data with quarter-sphere support vector machines
Detection of intrusions and malware & vulnerability assessment, GI SIG SIDAR workshop, DIMVA 2004
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 ...