A challenge in using machine learning for tasks such as network intrusion detection and fault diagnosis is the difficulty in obtaining clean data for training in order to model the normal behavior of the *** anomaly d...
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A challenge in using machine learning for tasks such as network intrusion detection and fault diagnosis is the difficulty in obtaining clean data for training in order to model the normal behavior of the *** anomalydetection techniques such as one class supportvectormachines (SVMs) have been introduced to overcome this *** class supportvectormachines model the normal or target data using non-linear surfaces in the input space while ignoring the anomalous *** approach to this problem is based on fitting a hyperellipsoid with a minimal effective radius,centered at the origin,around a majority of the data vectors in a higher dimensional *** formulate this as a linear optimisation problem,which is advantageous in terms of its computational *** demonstrate using real data from the Great Duck Island Project that our approach achieves better detection performance and flexibility in terms of parameter selection,compared to an earlier detection scheme using a quarter sphere SVM.
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