Abstract
Network security represents a keystone to ISPs, who need to cope with an increasing number of network attacks that put the network's integrity at risk. The high-dimensionality of network data provided by current network monitoring systems opens the door to the massive application of machine learning approaches to improve the detection and classification of network attacks. In this paper we devise a novel attacks detection and classification technique based on semi-supervised Machine Learning (ML) algorithms to automatically detect and diagnose network attacks with minimal training, and compare its performance to that achieved by other well-known supervised learning detectors. The proposed solution is evaluated using real network measurements coming from the WIDE backbone network, using the well-known MAWILab dataset for attacks labeling.
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