Federated AI-Based Cyber Threat Intelligence and Autonomous Defense for Distributed Cloud Infrastructure and Networks
DOI:
https://doi.org/10.15680/IJMRSETM.2026.0207005Keywords:
Federated learning, artificial intelligence, artificial intelligenceautonomous defense, distributed cloud, cloud security, machine learning, threat detection, privacy-preserving AI, adaptive cybersecurityAbstract
The rapid adoption of distributed cloud infrastructure, edge computing, multi-cloud platforms, and software-defined networks has significantly increased the complexity of enterprise cybersecurity. Conventional centralized threat-intelligence systems face challenges related to data volume, privacy, interoperability, latency, and the inability to effectively detect attacks distributed across geographically separated environments. Federated artificial intelligence (AI) provides an alternative by enabling multiple organizations, cloud domains, or network nodes to collaboratively train threat-detection models without directly exchanging sensitive security data. This essay proposes a federated AI-based cyber threat intelligence and autonomous defense framework for distributed cloud infrastructure and networks. The proposed approach combines federated learning, machine learning, behavioral analytics, threat-intelligence sharing, autonomous AI agents, and adaptive response mechanisms. Participating cloud environments locally analyze security telemetry and contribute model updates to a federated coordination layer, thereby supporting collaborative detection while reducing exposure of sensitive information. Autonomous defense agents subsequently use aggregated intelligence to identify threats, assess risk, coordinate investigations, and execute proportionate mitigation actions. The proposed research methodology employs a controlled distributed-cloud testbed, simulated cyberattacks, comparative experimentation, and quantitative evaluation using detection accuracy, precision, recall, F1-score, false-positive rate, communication overhead, detection latency, and response effectiveness. The framework aims to improve collective cyber defense, preserve data privacy, reduce detection delays, and strengthen the resilience of distributed cloud networks against evolving and coordinated cyber threats
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