Multi-task Learning for Intrusion Detection and Analysis of Computer Network Traffic

Abstract

Accurate identification of malicious computer network traffic is a challenging task for a number of reasons. This is especially highlighted when a new type of attack is launched because the amount of available data that belongs to this attack can be scarce. Having small amounts of such data makes understanding the behaviour of traffic and building models to accurately discover it more difficult. In this paper we present a novel classification method based on multi-task learning for the accurate identification of malicious network traffic even when little amount of training data is available. We show the effectiveness of our method by carrying out several experiments and comparisons with existing methods using open source data. Our results show that our method outperforms those methods especially when training data is scarce. Particularly, it achieves accuracy values of 98.51% and 99.76% on two computer network traffic dataset settings, whereas a start-ofthe-art algorithm achieves accuracy values of 93.56% and 96.25% on the same settings.