FGLIoT:基于联邦图学习和时空特征融合的物联网设备识别

IF 7.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xuhui Wang, Guanglu Sun, Xin Liu
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引用次数: 0

摘要

设备孤岛问题对物联网(IoT)的管理和安全提出了重大挑战。解决这一问题的关键是在保护数据隐私的同时准确识别连接到网络的物联网设备。然而,现有的解决方案忽略了包间的语义相关性,这使得它们无法充分探索设备通信流量中潜在的行为模式。因此,我们提出了一种基于联邦图学习的物联网设备识别方法FGLIoT。FGLIoT首先将物联网设备产生的通信流量数据表示为数据包序列图,保留了数据包的语义信息。然后,它使用图形学习模块来捕获包间语义相关性并学习设备通信行为的表示。随后,空间和时间特征提取器分别对表征进行处理,以捕获它们的空间相关性和时间依赖性。最后,利用残差连接将行为表征与其时空特征融合,生成用于物联网设备识别的行为指纹。在三个公共物联网设备数据集上的实验结果证明了FGLIoT在解决设备孤岛问题方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FGLIoT: IoT device identification via federated graph learning and spatio-temporal feature fusion
The device silo problem poses a significant challenge to the management and security of the Internet of Things (IoT). The key to solving this issue is to accurately identify IoT devices connected to the network while protecting data privacy. However, existing solutions overlook inter-packet semantic correlations, a fact which renders them unable to fully explore the potential behavior patterns in device communication traffic. Therefore, we propose FGLIoT, a federated graph learning-based method for IoT device identification. FGLIoT first represents the communication traffic data generated by IoT devices as packet sequence graphs, preserving the semantic information of packets. It then employs a graph learning module to capture inter-packet semantic correlations and learn representations of device communication behaviors. Subsequently, the representations are processed by spatial and temporal feature extractors to capture their spatial correlations and temporal dependences, respectively. Finally, residual connections are used to fuse the behavior representations with their spatial and temporal features, generating behavioral fingerprints for IoT device identification. Experimental results on three public IoT device datasets demonstrate the effectiveness of FGLIoT in solving the device silo problem.
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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