Federated Threat Intelligence Frameworks for Autonomous Network De-fense in Cloud-Edge Environments Using Secure Computation
Keywords:
Federated threat intelligence, cloud-edge security, secure computation, autonomous network defense, privacy-preserving machine learningAbstract
As cloud-edge computing becomes the backbone of digital infrastructures, autonomous network defense mechanisms must be enhanced to counter sophisticated cyber threats. Traditional centralized threat intelligence models suffer from security vulnerabilities, latency issues, and inefficiency in handling distributed attacks. This paper explores federated threat intelligence frameworks leveraging secure computation to enhance autonomous network defense in cloud-edge environments. The study presents a comprehensive literature review of pre-2023 research, analyzes recent advances in federated learning and privacy-preserving computation, and provides insights into future research directions. The proposed approach integrates distributed threat intelligence sharing, cryptographic privacy mechanisms, and adaptive defense strategies for resilient security management.
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Copyright (c) -1 Aybars Gkioulos (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
