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@article{192295,
        author = {Keerthika N A},
        title = {CYBERMIND: AI-Enabled Zero Trust Intrusion Detection for Industrial IoT Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {856-859},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192295},
        abstract = {Industrial Internet of Things (IIoT) technologies are rapidly transforming traditional industries by enabling real-time monitoring, automation, and intelligent decision-making. However, the extensive deployment of interconnected industrial devices has significantly increased the attack surface, exposing critical infrastructure to advanced cyber threats. Conventional perimeter-based security mechanisms are inadequate for protecting Industrial IoT environments due to dynamic network behavior, heterogeneous devices, and insider threats.
This paper presents CYBERMIND, an AI-enabled intrusion detection framework integrated with Zero Trust Architecture (ZTA) to secure Industrial IoT networks. The proposed system employs a Logistic Regression-based anomaly detection model to continuously monitor network traffic and classify device behavior into trusted, suspicious, and malicious categories. Zero Trust principles are enforced to ensure continuous authentication, authorization, and behavioral verification of all devices.
Experimental evaluation demonstrates that CYBERMIND achieves higher detection accuracy, improved precision and recall, and a significantly lower false positive rate compared to traditional intrusion detection systems. The results confirm that CYBERMIND provides a lightweight, scalable, and effective cybersecurity solution suitable for real-time Industrial IoT deployments.},
        keywords = {Industrial IoT, Zero Trust Architecture, Intrusion Detection System, Machine Learning, Cybersecurity.},
        month = {February},
        }