Federated Learning-Based Threat Intelligence Sharing Across Organizations for Proactive Cyber Defence

Registration ID: IJNRD_309716

Published ID: IJNRD2510107

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Latesh Gautam

federated learning, threat intelligence, proactive cyber defense, privacy preservation, collaborative security

Cyber threats are escalating in complexity and frequency, demanding that organizations transition from isolated defense postures to collaborative and predictive security ecosystems. Conventional methods of threat intelligence exchange face persistent barriers related to data privacy, regulatory constraints, and inter-organizational trust. Moreover, centralized architectures for information sharing expose critical vulnerabilities, rendering them susceptible to data breaches and malicious interference. In this context, Federated Learning (FL) emerges as an innovative approach that allows multiple entities to co-train robust machine learning models while retaining sensitive data within their own environments. This research explores the integration of FL into cyber threat intelligence systems, proposing a novel, privacy-preserving framework that facilitates secure, distributed learning without compromising confidentiality. The study further assesses how FL can strengthen collective situational awareness and accelerate adaptive response mechanisms across heterogeneous organizational networks. By leveraging decentralized collaboration, the proposed model aims to redefine proactive cyber defense and establish a scalable foundation for future threat intelligence ecosystems.

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Latesh Gautam (October-2025). Federated Learning-Based Threat Intelligence Sharing Across Organizations for Proactive Cyber Defence. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(10), b52-b58. https://ijnrd.org/papers/IJNRD2510107.pdf

Volume 10 Issue 10, October-2025

Pages : b52-b58

Paper Reg. ID: IJNRD_309716

Published Paper Id: IJNRD2510107

Research Area: Science and Technology

Author Type: Indian Author

Country: balaghat, madhyapradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2510107.pdf

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