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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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Cognitive Zero-Trust Networks Using Federated Threat Learning

Jyotsna Pandit
Professor, Manav Rachna University, Faridabad , India .
Preeti Kumari
Assistant Professor, Department of CSE & Apex, Chandigarh Engineering College, CGC Jhajeri, India.

Keywords: Cognitive Zero-Trust Architecture, Federated Threat Learning, Intrusion Detection System, IoT Security, Cyber Threat Intelligence, Reinforcement Learning, Zero-Day Attack Detection, Trust Computation

Abstract

The dynamism and proliferation of IoT, edge, and cyber physical systems has brought complexity to contemporary networks as well as brought them close to extremely dynamic and dynamic cyber threats. Conventional intrusion detection infrastructure, as well as ancient Zero-Trust architecture, cannot work in those heterogeneous settings because it relies on centrally stored data, fixed trust analysis, and low flexibility in countering the zero-day assaults. To overcome those issues, Cognitive Zero-Trust Network based on Federated Threat Learning (C-ZTN-FTL), a smart, privacy-obliging framework, is proposed in this paper, which combines federated learning, cognitive trust calculation, and multi-layer fusion of threat knowledge. The model is proposed, which also has a Federated Threat Learning mechanism that combines encrypted gradients and latent threat embeddings so that the representation of the threat can be richer and generalize better across non-IID data environments. A Cognitive Trust Engine based on reinforcement-learning constantly updates the trust scores on the basis of local anomaly patterns, global threat settings, device posture, and history, providing context-based and dynamic Zero-Trust access control. The reasoning over unknown or new attack patterns is also improved further by multi-layer threat intelligence fusion at the level of IoT, edge, and cloud levels. As shown by the experimental findings based on the CIC-IDS-2017, UNSW-NB15, and IoTID20 datasets, the proposed system has a detection accuracy of 97.2, indicates a significant enhancement of the zero-day attack detection, a lower false-positive rate, more stable trust scoring by 18.3, and a reduction in the communication overhead by 35% relative to the baseline FL-IDS models. On the whole, the C-ZTN-FTL framework offers dynamic, scalable, and resilient solution to secure the contemporary distributed networks against high-level cyber threats.
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Published
2025-12-26
Pages
1-12
Issue
Vol. 4 No. 4 (2025): IJRTTE - 04 - 04
Section
Articles

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