【Author】
Li, Kun; Zhou, Huachun; Tu, Zhe; Liu, Feiyang; Zhang, Hongke
【Source】SECURITY AND COMMUNICATION NETWORKS
【Abstract】The malicious flow originating from massive access devices in 6G network will increase sharply. In order to effectively reduce malicious flow, we hope to establish a new framework for coordination of security monitoring and malicious behaviour control in 6G network. Federated learning provides data and privacy protection for the distributed network security behaviour knowledge base. However, since the equipment of its participants needs to upload the original data to the central server for model training, this may lead to data leakage in the knowledge base. Therefore, in this article, we first use the knowledge graph to describe network security behaviours, then build a universal network security malicious behaviour knowledge base, and discuss its application scenarios. Then, we propose a blockchain empowered federated learning (BeFL) for distributed network security malicious behaviour knowledge base architecture to ensure the security of knowledge transmission. Finally, we deployed the designed distributed knowledge base in the prototype system and compared it with the other two baseline methods to verify the performance. Relevant results show that our method outperforms other methods in terms of user identification, flow detection, and attack source tracing.
【标题】6G 分布式网络安全行为知识库的区块链赋能联邦学习
【摘要】来自6G网络海量接入设备的恶意流量将急剧增加。为了有效减少恶意流量，我们希望建立一个新的6G网络安全监控和恶意行为控制协调框架。联邦学习为分布式网络安全行为知识库提供数据和隐私保护。但是，由于其参与者的设备需要将原始数据上传到中心服务器进行模型训练，这可能会导致知识库中的数据泄露。因此，在本文中，我们首先使用知识图谱来描述网络安全行为，然后构建一个通用的网络安全恶意行为知识库，并讨论其应用场景。然后，我们提出了一种基于区块链的联邦学习（BeFL）分布式网络安全恶意行为知识库架构，以保证知识传输的安全性。最后，我们将设计的分布式知识库部署在原型系统中，并与其他两种基线方法进行比较，以验证性能。相关结果表明，我们的方法在用户识别、流量检测和攻击溯源方面优于其他方法。
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