Jianping Wu, Guangqiu Qiu, Chunming Wu, Weiwei Jiang, Jiahe Jin
College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China.
Smart Government R &D Center (Laboratory) of Hangzhou Dianzi University, Hangzhou, 310018, China.
Sci Rep. 2024 Aug 17;14(1):19088. doi: 10.1038/s41598-024-70032-2.
Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks. This method enables collaborative model training while protecting data privacy on distributed devices. By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby improving the precision of internal network interactions. Experimental results demonstrate that our method achieves comparable accuracy and robustness to traditional detection methods while prioritizing privacy protection and data security.
联邦学习是解决机器学习中数据隔离和隐私泄露问题的有效解决方案。然而，由于网络攻击，确保部署联邦学习的网络设备和架构的安全性仍然是一个挑战。本文提出了一种基于注意力的图神经网络，用于检测跨级别和跨部门的网络攻击。该方法在保护分布式设备上的数据隐私的同时，实现了协作模型训练。通过按时间顺序组织网络流量信息，并基于日志密度构建图结构，提高了网络攻击检测的准确性。引入注意力机制并构建联邦图注意力网络（FedGAT）模型，用于评估图中节点之间的交互性，从而提高内部网络交互的精度。实验结果表明，我们的方法在优先考虑隐私保护和数据安全的同时，与传统检测方法具有相当的准确性和鲁棒性。
