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SoK: The Impact of Unlabelled Data in Cyberthreat Detection by Apruzzese, Giovanni; Laskov, Pavel; Tastemirova, Aliya is a scholarly article available to read on EtoBox.

What is SoK: The Impact of Unlabelled Data in Cyberthreat Detection about?

Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD) in the recent years. A substantial research effort has been invested in the development of specialized algorithms for CTD tasks. From the operational perspective, however, the progress of ML-based CTD is hindered by the difficulty in obtaining the large sets of labelled data to train ML detectors. A potential solution to this problem are semisupervised learning (SsL) methods, which combine small labelled datasets with large amounts of unlabelled data. This paper is aimed at systematization of existing work on SsL for CTD and, in particular, on understanding the utility of unlabelled data in such systems. To this end, we analyze the cost of labelling in various CTD tasks and develop a formal cost model for SsL in this context. Building on this foundation, we formalize a set of requirements for evaluation of SsL methods, which elucidates the contribution of unlabelled data. We review the state-of-the-art and observe that no previous work meets such requirements. To address this problem, we propose a framework for assessing the benefits of unlabelled data in SsL. We showcase an application of this f

Author

Apruzzese, Giovanni; Laskov, Pavel; Tastemirova, Aliya

Published

2022

Language

EN