Con2Mix: A semi-supervised method for imbalanced tabular security data¹

Issue title: Selected papers from the 36th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy – DBSEC 2022

Guest editors: Shamik Sural and Haibing Lu

Article type: Research Article

Authors: Li, Xiaodi^{a; *} | Khan, Latifur^b | Zamani, Mahmoud^b | Wickramasuriya, Shamila^b | Hamlen, Kevin^b | Thuraisingham, Bhavani^b

Affiliations: [a] Department of Electrical and Computer Engineering, The University of Texas at Dallas, TX, USA | [b] Computer Science Department, The University of Texas at Dallas, TX, USA

Correspondence: [*] Corresponding author. E-mail: [email protected].

Note: [1] This paper is an extended and revised version of a paper presented at DBSEC 2022.

Abstract: Con2Mix (Contrastive Double Mixup) is a new semi-supervised learning methodology that innovates a triplet mixup data augmentation approach for finding code vulnerabilities in imbalanced, tabular security data sets. Tabular data sets in cybersecurity domains are widely known to pose challenges for machine learning because of their heavily imbalanced data (e.g., a small number of labeled attack samples buried in a sea of mostly benign, unlabeled data). Semi-supervised learning leverages a small subset of labeled data and a large subset of unlabeled data to train a learning model. While semi-supervised methods have been well studied in image and language domains, in security domains they remain underutilized, especially on tabular security data sets which pose especially difficult contextual information loss and balance challenges for machine learning. Experiments applying Con2Mix to collected security data sets show promise for addressing these challenges, achieving state-of-the-art performance on two evaluated data sets compared with other methods.

Keywords: Semi-supervised learning, contrastive learning, tabular data sets, security data sets

DOI: 10.3233/JCS-220130

Journal: Journal of Computer Security, vol. 31, no. 6, pp. 705-726, 2023