Proactive Cyber Threat Intelligence using Advanced Cloud Analytics for Distributed Network Environments

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Published: 2026-07-24
DOI: https://doi.org/10.15662/IJRPETM.2026.0904010
Keywords:
Cyber threat intelligence, cloud analytics, cybersecurity, distributed networks, threat detection, machine learning, anomaly detection, cloud security, predictive analytics, network security, security information and event management, threat intelligence

Main Article Content

Dr.Jeeva Kathiravan
Professor, Department of Information Technology, Velammal Engineering College, Chennai, India

Abstract

The rapid expansion of cloud computing, distributed networks, Internet of Things devices, remote work environments, and interconnected enterprise applications has significantly increased the complexity of modern cybersecurity. Conventional threat detection approaches often rely on predefined signatures, isolated monitoring systems, and reactive responses, limiting their ability to identify emerging and previously unknown threats. Proactive cyber threat intelligence supported by advanced cloud analytics offers an alternative approach by continuously collecting, integrating, and analyzing security data from heterogeneous distributed environments. This study examines the application of cloud-based analytics, machine learning, behavioral analysis, threat intelligence feeds, and automated correlation techniques for proactive cyber threat identification. The proposed approach considers security data from network traffic, endpoint activities, authentication events, cloud workloads, application logs, and external intelligence sources as interconnected evidence rather than isolated events. Advanced analytics can assist security teams in identifying anomalous behavior, discovering attack patterns, prioritizing threats, and generating early warnings before incidents develop into significant security breaches. The research adopts a qualitative conceptual methodology based on a structured synthesis of cybersecurity, cloud analytics, threat intelligence, and anomaly-detection literature. The study develops an analytical framework integrating distributed data collection, cloud-based processing, intelligence enrichment, predictive analysis, risk prioritization, and security response. Particular attention is given to scalability, data privacy, model accuracy, interoperability, explainability, and operational resilience. The study provides a foundation for developing proactive and adaptive cyber threat intelligence architectures for complex distributed network environments

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Article Details

Issue

Vol. 9 No. 4 (2026): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

Proactive Cyber Threat Intelligence using Advanced Cloud Analytics for Distributed Network Environments. (2026). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(4), 1636-1644. https://doi.org/10.15662/IJRPETM.2026.0904010

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