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@article{182121,
        author = {Rushdha V and Shada Ali Kuzhikattil and Dr. Priya. P. Sajan},
        title = {Insider Threat Detection Using AI-Based Behaviour Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1061-1065},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182121},
        abstract = {Insider threats pose a unique cyber security challenge, due to the authorized nature of access making malicious actions difficult to distinguish from normal behaviour. With the rising impact of insider incidents and the limitations of rule-based approaches, there is a critical need for adaptive and intelligent detection solutions. This research proposes an AI-driven behaviour analytics framework that analyses patterns in user activity to detect anomalies suggestive of insider threats. Using the CERT Insider Threat Dataset and a Random Forest classifier, the proposed system achieves accurate, interpretable, and computationally efficient threat detection. This Adaptive AI-driven framework emphasizes early threat identification, minimizing false positives, and ensures feasibility for real-world deployment in resource constrained environments.},
        keywords = {Insider Threat Detection, Behaviour Analytics, Machine Learning, Random Forest, Anomaly Detection.},
        month = {July},
        }