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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.<\/jats:p>","DOI":"10.3233\/jcs-220130","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:19:58Z","timestamp":1694776798000},"page":"705-726","source":"Crossref","is-referenced-by-count":2,"title":["Con2Mix: A semi-supervised method for imbalanced tabular security data1"],"prefix":"10.1177","volume":"31","author":[{"given":"Xiaodi","family":"Li","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of Texas at Dallas, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Latifur","family":"Khan","sequence":"additional","affiliation":[{"name":"Computer Science Department, The University of Texas at Dallas, TX, 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