Real Time Attack Detection with Deep Learning

Abstract

The Internet of Things (IoT) significantly extends the attack surface of the Internet, making the use of an Anomaly-based Intrusion Detection System of paramount importance. Despite in the last years big research efforts have focused on the application of Deep Learning techniques to attack detection, an ultimate real-time solution, able to provide a high detection rate with an acceptable false alarm rate while processing raw network traffic in real time, has still to be identified. For this reason, in this paper we propose an Intrusion Detection System that, leveraging on probabilistic data structures and Deep Learning, 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.