An anomaly-based network intrusion detection system using deep learning

Anomaly-based Intrusion Detection is a key research topic in network security due to its ability to face unknown attacks and new security threats. For this reason, many works on the topic have been proposed in the last decade. Nonetheless, an ultimate solution, able to provide a high detection rate with an acceptable false alarm rate, has still to be identified. In the last years big research efforts have focused on the application of Deep Learning techniques to the field, but no work has been able, so far, to propose a system achieving good detection performance, while processing raw network traffic in real time. For this reason, this thesis proposes an Intrusion Detection System that, leveraging on probabilistic data structures and several Deep Learning techniques, is able to process in real time the traffic collected in a backbone network, offering almost optimal detection performance and low false alarm rate. Indeed, the extensive experimental tests, run to validate and evaluate our system, confirm that, with a proper parameter setting, we can achieve about 90% of detection rate, with an accuracy of 0.871