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Enhancing adversarial robustness of IoT intrusion detection via SHAP-based attribution fingerprinting

DOITrouver le DOI : https://doi.org/10.1109/Trustcom66490.2025.00125
AuteurRechercher : 1; Rechercher : 1; Rechercher : 1; Rechercher : 2; Rechercher : 3Identifiant ORCID : https://orcid.org/0000-0002-3460-6946
Affiliation
  1. York University
  2. University of Guelph
  3. Conseil national de recherches Canada. Technologies numériques
Bailleur de fondsRechercher : National Research Council
FormatTexte, Article
Conférence2025 IEEE 24th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), November 14-17, 2025, Guiyang, China
Sujetintrusion detection; robustness; trust-worthiness; adversarial detection; attribution finger-printing; explainability; interpretability; adversarial machine learning; adversarial attacks; Internet of Things
Résumé
The rapid proliferation of Internet of Things (IoT) devices has transformed numerous industries by enabling seamless connectivity and data-driven automation. However, this expansion has also exposed IoT networks to increasingly sophisticated security threats, including adversarial attacks targeting artificial intelligence (AI) and machine learning (ML)-based intrusion detection systems (IDS) to deliberately evade detection, induce misclassification, and systematically undermine the reliability and integrity of security defenses. To address these challenges, we propose a novel adversarial detection model that enhances the robustness of IoT IDS against adversarial attacks through SHapley Additive exPlanations (SHAP)-based fingerprinting. Using SHAP’s DeepExplainer, we extract attribution fingerprints from network traffic features, enabling the IDS to reliably distinguish between clean and adversarially perturbed inputs. By capturing subtle attribution patterns, the model becomes more resilient to evasion attempts and adversarial manipulations. We evaluated the model on a standard IoT benchmark dataset, where it significantly outperformed a state-of-the-art method in detecting adversarial attacks. In addition to enhanced robustness, this approach improves model transparency and interpretability, thereby increasing trust in the IDS through explainable AI.
Date de publication
Maison d’éditionInstitute of Electrical and Electronics Engineers
Dans
  • 2025 IEEE 24th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 11354855 (2 février 2026) : 1080–1091.
Langueanglais
Publications évaluées par des pairsOui
Exporter la noticeExporter en format RIS
Signaler une correctionSignaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrementeab5636c-391e-47cd-a030-f6ac9c73ec7b
Enregistrement créé2026-04-16
Enregistrement modifié2026-06-03

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