Which algorithm can detect unknown attacks? Comparison of supervised, unsupervised and meta-learning algorithms for intrusion detection

Abstract

There is an astounding growth in the adoption of machine learners (MLs) to craft intrusion detection systems (IDSs). These IDSs model the behavior of a target system during a training phase, making them able to detect attacks at runtime. Particularly, they can detect known attacks, whose information is available during training, at the cost of a very small number of false alarms, i.e., the detector suspects attacks but no attack is actually threatening the system. However, the attacks experienced at runtime will likely differ from those learned during training and thus will be unknown to the IDS. Consequently, the ability to detect unknown attacks becomes a relevant distinguishing factor for an IDS. This study aims to evaluate and quantify such ability by exercising multiple ML algorithms for IDSs. We apply 47 supervised, unsupervised, deep learning, and meta-learning algorithms in an experimental campaign embracing 11 attack datasets, and with a methodology that simulates the occurrence of unknown attacks. Detecting unknown attacks is not trivial: however, we show how unsupervised meta-learning algorithms have better detection capabilities of unknowns and may even outperform classification performance of other ML algorithms when dealing with unknown attacks.