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TRV-2026-0201Certified recordPeer-reviewed

XAI-driven Data Mining for Self-defending IoT Systems: Enhancing Cybersecurity Transparency in the Age of Smart Cities

The rapid expansion of Internet of Things (IoT) technologies in smart cities, healthcare, and industrial automation has intensified the need for cybersecurity frameworks capable of operating at scale and in real time under increasingly sophisticated threat conditions. Traditional security mechanisms and opaque AI-based models are no longer adequate for protecting interconnected urban infrastructures, especially as regulatory and societal expectations move toward transparency and accountability. Although prior su…

Crime · The Trace — both readings · certified 2026-07-13 · v1 · article view · machine-readable

Current reading — gain

XAI-driven data mining applied to IoT ecosystems can detect anomalies and support automated security decisions through transparent and interpretable reasoning for smart city infrastructure.

Current reading — problem

Traditional and opaque AI security mechanisms are inadequate for protecting interconnected urban IoT infrastructures, facing challenges of data privacy, scalability, computational constraints, and limited interpretability.

What this doesn’t fix

Review identifies persistent constraints to deployment including privacy, scalability, and computational limits of current models.

Evidence

Reader signal

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Truvace Impact Record TRV-2026-0201, v1: “XAI-driven Data Mining for Self-defending IoT Systems: Enhancing Cybersecurity Transparency in the Age of Smart Cities.” Truvace, 2026-07-13. /record/TRV-2026-0201 (accessed at citation time). sha256 530cd2c4e0f184c6…

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