aceSelf-Supervised One-Class Tabular Deep Learning for Intrusion Detection in Artificial Intelligence of Things Systems
The emergence of the artificial intelligence of things (AIoT) has led to increasingly sophisticated cyberattacks. For many of these threats, labeled training data are scarce or unavailable, which increases the need for label-free intrusion detection systems. To address this issue, this study proposes a self-supervised one-class TabNet framework, termed SS-OC-TabNet, for intrusion detection in tabular network traffic. The framework combines self-supervised representation learning and one-class compactness learnin…
A two-stage self-supervised one-class model learns normal network traffic without labeled attacks and flags anomalies, reaching 93.20% accuracy with a 2.13% false alarm rate in the authors' evaluation.
Evidence
- Peer-reviewedJournal of Computational and Cognitive Engineering2026-10-15
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Truvace Impact Record TRV-2026-1656, v1: “Self-Supervised One-Class Tabular Deep Learning for Intrusion Detection in Artificial Intelligence of Things Systems.” Truvace, 2026-10-17. /record/TRV-2026-1656 (accessed at citation time). sha256 9870be8af43deaef…
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