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Statistical machine learning techniques have recently garnered increased popularity as a means to improve network design and security. For intrusion detection, such methods build a model for normal behavior from training data and detect attacks as deviations from that model. This process invites adversaries to manipulate the training data so that the learned model fails to detect subsequent attacks. The authors evaluate poisoning techniques and develop a defense, in the context of a particular anomaly detector - namely the PCA-subspace method for detecting anomalies in backbone networks.
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