On the detection of lateral movement through supervised machine learning and an open-source tool to create turnkey datasets from Sysmon logs

Abstract

Lateral movement (LM) is a principal, increasingly common, tactic in the arsenal of advanced persistent threat (APT) groups and other less or more powerful threat actors. It concerns techniques that enable a cyberattacker, after establishing a foothold, to maintain ongoing access and penetrate further into a network in quest of prized booty. This is done by moving through the infiltrated network and gaining elevated privileges using an assortment of tools. Concentrating on the MS Windows platform, this work provides the first to our knowledge holistic methodology supported by an abundance of experimental results towards the detection of LM via supervised machine learning (ML) techniques. We specifically detail feature selection, data preprocessing, and feature importance processes, and elaborate on the configuration of the ML models used. A plethora of ML techniques are assessed, including 10 base estimators, one ensemble meta-estimator, and five deep learning models. Vis-à-vis the relevant literature, and by considering a highly unbalanced dataset and a multiclass classification problem, we report superior scores in terms of the F1 and AUC metrics, 99.41% and 99.84%, respectively. Last but not least, as a side contribution, we offer a publicly available, open-source tool, which can convert Windows system monitor logs to turnkey datasets, ready to be fed into ML models.