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Anomaly-Driven Security Intelligence for Proactive Cyber Threat Mitigation

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Anomaly-Driven Security Intelligence for Proactive Cyber Threat Mitigation


Shivaraj Yanamandram Kuppuraju | Vasudev Karthik Ravindran | Vineet Baniya



Shivaraj Yanamandram Kuppuraju | Vasudev Karthik Ravindran | Vineet Baniya "Anomaly-Driven Security Intelligence for Proactive Cyber Threat Mitigation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-4, August 2025, pp.530-536, URL: https://www.ijtsrd.com/papers/ijtsrd97237.pdf

This paper presents a comprehensive study on anomaly-driven security intelligence as a proactive approach to cyber threat mitigation, emphasizing the growing need for adaptive, intelligent systems capable of detecting emerging and unknown attacks in increasingly complex digital environments. Traditional signature-based detection methods fall short in addressing modern threats such as zero-day exploits and advanced persistent threats, prompting the integration of machine learning and deep learning techniques into cybersecurity frameworks. The research explores multiple anomaly detection models, including Isolation Forest, Autoencoders, LSTM networks, and an ensemble of Autoencoder-LSTM, applied to benchmark datasets. Results reveal that the ensemble model outperforms others in precision, recall, F1-score, and AUC-ROC, demonstrating its effectiveness in accurately identifying anomalies with reduced false positives. The study also discusses operational considerations, model interpretability, and limitations such as threshold tuning and adversarial robustness. By validating the utility of anomaly-based models in real-time detection systems, this paper supports the transition from reactive to proactive cybersecurity and sets the foundation for future work on explainable, resilient, and scalable threat detection frameworks.

Anomaly Detection, Cybersecurity, Machine Learning, Threat Mitigation, Security Intelligence.


IJTSRD97237
Volume-9 | Issue-4, August 2025
530-536
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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