aceXAI-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…
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.
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.
Review identifies persistent constraints to deployment including privacy, scalability, and computational limits of current models.
Evidence
- Peer-reviewedInternational Journal of Innovative Science and Research Technology2025-10-10
- Peer-reviewedCognitive Computation2026-02-20
- Peer-reviewedJournal of Cybersecurity and Privacy2025-11-05
- Peer-reviewedInternational Journal of Scientific Research in Science and Technology2024-11-17
- Peer-reviewedCommunications Materials2026-02-17
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Truvace Impact Record TRV-2026-0201, v5: “XAI-driven Data Mining for Self-defending IoT Systems: Enhancing Cybersecurity Transparency in the Age of Smart Cities.” Truvace, 2026-10-11. /record/TRV-2026-0201 (accessed at citation time). sha256 71a3745edadf672a…
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