Paper Title
Insider Threat Detection through User Activity Monitoring and Risk Evaluation
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Registration ID: IJNRD_322306
Published ID: IJNRD2603432
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Abstract
Abstract: Detecting insider threats is difficult, not because the detection technology is inadequate, it is simply that the attacker is already inside, authenticated, trusted, and using exactly the same channels as the normal users. The problem is that the traditional detection mechanisms are ineffective against insider threats because signature-based approaches depend on known attacks and struggle to identify new or unknown attacks. In this paper we propose a detection system called Insider Sentinel, that is centered around behavioral activity rather than static rules. We extracted activity records from the CERT r4.2 dataset and converted each user’s daily activities into signals such as logon frequency, off-hours logons, usb connects, file deletion, and email count, which are then passed through four different models. Isolation Forest detects the outliers without needing labeled incident history, the Random Forest and XGBoost use labeled known attack patterns for classification, whereas the LSTM uses a sequence of fourteen days at a time for identifying the insiders. The outputs of these four models are combined using a weighted ensemble at threshold of 0.53, this creates a single risk score for each user. The ensemble has an ROC-AUC of 0.9647, a recall of 94.74%, and the risk scores are categorized into four level: Low, Suspicious, High and Critical. These are visualized through a security monitoring dashboard that handles alerts, investigations, and incident tracking with SHAP based explanations. Our proposed framework emphasizes the fact that only accurate detection is not sufficient but also needs interpretable evidence that supports the security analyst during investigation and response.
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How To Cite (APA)
Sadia Begum, Shagufta Fatima, Umme Madiha, & Dr.M. Sowmya (March-2026). Insider Threat Detection through User Activity Monitoring and Risk Evaluation. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(3), e210-e217. https://ijnrd.org/papers/IJNRD2603432.pdf
Issue
Volume 11 Issue 3, March-2026
Pages : e210-e217
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Paper Reg. ID: IJNRD_322306
Published Paper Id: IJNRD2603432
Research Area: Other area not in list
Author Type: Indian Author
Country: Hyderabad, Telangana, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2603432.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2603432
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