Volume 5 • Issue 2 • PP: 13–21 • 2025
From Packet Traces to Contradiction Scores: A Neutrosophic Signature Calculus for Real-Time IoT Intrusion Attribution
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Real-time Internet of Things intrusion attribution is often formulated as direct multi-class classification, although packet traces contain incomplete, conflicting, and imbalanced evidence. This paper develops a mathematical neutrosophic signature calculus in which each flow is represented by truth, indeterminacy, and falsity memberships over class-specific attack signatures. The proposed model constructs entropy-contrast behavioral channels, maps each flow to class prototypes through a contradiction-aware single-valued neutrosophic transformation, and derives a closed-form attribution rule by coupling prototype truth, opposite-region falsity pressure, and explicit indeterminacy penalization. The study uses RT-IoT2022, a public UCI benchmark donated in 2024 with 123,117 flows, 83 features, and 12 normal/attack labels. The results show that the proposed calculus provides interpretable class attribution and stable macro-level behavior under severe class imbalance. The work supports neutrosophic signature modeling as a transparent route for IoT security decision support under inconsistent network evidence.
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References
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