EXVul: Toward Effective and Explainable Vulnerability Detection for IoT Devices

• Published in: IEEE internet of things journal Vol. 11; no. 12; pp. 22385 - 22398
• Main Authors: Cao, Sicong, Sun, Xiaobing, Liu, Wei, Wu, Di, Zhang, Jiale, Li, Yan, Luan, Tom H., Gao, Longxiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Codes; Combinatorial analysis; Contrastive learning; Contrastive learning (CL); Cybersecurity; Deep learning; Detection algorithms; explainability; Internet of Things; Internet of Things (IoT); Multiprotocol label switching; Reactive power; Self-supervised learning; stability; Stability analysis; Training

As with anything connected to the Internet, Internet of Things (IoT) devices are also subject to severe cybersecurity threats because an adversary could exploit vulnerabilities in their internal software to perform malicious attacks. Despite the promising results of deep learning (DL)-based approach...