Generative Methods for Out-of-distribution Prediction and Applications for Threat Detection and Analysis: A Short Review

In recent times, Machine Learning has played an important role in developing novel advanced tools for threat detection and mitigation. Intrusion Detection, Misinformation, Malware, and Fraud Detection are just some examples of cybersecurity fields in which Machine Learning techniques are used to reveal the presence of malicious behaviors. However, Out-of-Distribution, i.e., the potential distribution gap between training and test set, can heavily affect the performances of the traditional Machine Learning based methods. Indeed, they could fail in identifying out-of-samples as possible threats, therefore devising robust approaches to cope with this issue is a crucial and relevant challenge to mitigate the risk of undetected attacks. Moreover, a recent emerging line proposes to use generative models to yield synthetic likely examples to feed the learning algorithms. In this work, we first survey recent Machine Learning and Deep Learning based solutions to face both the problems, i.e., outlier detection and generation; then we illustrate the main cybersecurity application scenarios in which these approaches have been adopted successfully.