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

DOIResolve DOI: https://doi.org/10.1109/Trustcom66490.2025.00125
AuthorSearch for: 1; Search for: 1; Search for: 1; Search for: 2; Search for: 3ORCID identifier: https://orcid.org/0000-0002-3460-6946
Affiliation
  1. York University
  2. University of Guelph
  3. National Research Council Canada. Digital Technologies
FunderSearch for: National Research Council
FormatText, Article
Conference2025 IEEE 24th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), November 14-17, 2025, Guiyang, China
Subjectintrusion detection; robustness; trust-worthiness; adversarial detection; attribution finger-printing; explainability; interpretability; adversarial machine learning; adversarial attacks; Internet of Things
Abstract
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.
Publication date
PublisherInstitute of Electrical and Electronics Engineers
In
  • 2025 IEEE 24th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 11354855 (2 February 2026): 1080–1091.
LanguageEnglish
Peer reviewedYes
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Record identifiereab5636c-391e-47cd-a030-f6ac9c73ec7b
Record created2026-04-16
Record modified2026-06-03

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