International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
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Volume 8 Issue 2
March-April 2026

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AI Tool for Detecting Behavioural Anomalies in Organisational Networks

Author(s) Mr. Akhil Shaikh
Country India
Abstract The increasing sophistication of cyber threats has rendered traditional signature-based and rule-based security mechanisms insufficient for protecting modern organisations. Advanced Persistent Threats (APTs), insider threats, and zero-day attacks often evade conventional detection systems by mimicking legitimate user behaviour. Behavioural Anomaly Detection (BAD), powered by Artificial Intelligence (AI) and Machine Learning (ML), has emerged as a critical cybersecurity approach that focuses on identifying deviations from normal behavioural patterns rather than known attack signatures. This paper explores the design, implementation, and effectiveness of AI-driven behavioural anomaly detection tools as a cybersecurity solution for organisations. We present a conceptual framework, discuss commonly used machine learning techniques, evaluate advantages and limitations, and highlight real-world applicability within cloud and enterprise environments. The findings indicate that behavioural anomaly detection significantly enhances organisational security posture by enabling early threat detection, reducing dwell time, and improving resilience against evolving cyber threats.
Keywords Behavioural Anomaly Detection, Cybersecurity, Artificial Intelligence, Machine Learning, Insider Threats, Zero-Day Attacks
Field Computer > Network / Security
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-04-10
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.73340
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E-ISSN 2582-2160
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