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BFLS: Blockchain and Federated Learning for sharing threat detection models as Cyber Threat Intelligence by Tongtong Jiang; Guowei Shen; Chun Guo; Yunhe Cui; Bo Xie is a Computer Science article available to read on EtoBox.

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Recently, Cyber Threat Intelligence (CTI) sharing has become an important weapon for cyber defenders to mitigate the increasing number of cyber attacks in a proactive and collaborative manner. However, with the dramatic increase in the deployment of shared communications between organizations, data has been a major priority to detect threats in the CTI sharing platform. In the modern environment, a valuable asset is the user’s threat data. Privacy policies are necessary to ensure the security of user data in the threat intelligence sharing community. Federated learning acts as a special machine learning technique for privacy preservation and offers to contextualize data in a CTI sharing platform. Therefore, this article proposes a new approach to threat intelligence sharing called BFLS (Blockchain and Federated Learning for sharing threat detection models as Cyber Threat Intelligence), where blockchain-based CTI sharing platforms are used for security and privacy. Federated learning technology is adopted for scalable machine learning applications, such as threat detection. Furthermore, users can obtain a well-trained threat detection model without sending personal data to the centr

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Author

Tongtong Jiang; Guowei Shen; Chun Guo; Yunhe Cui; Bo Xie

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)