GDI: A Novel IoT Device Identification Framework via Graph Neural Network-Based Tensor Completion

doi:10.1109/TSC.2024.3463496

	GDI: A Novel IoT Device Identification Framework via Graph Neural Network-Based Tensor Completion
	Wang, Haoxuan 1,2; Xie, Kun 1,2; Wang, Xin 3; Wen, Jigang 4; Xie, Ruotian 1,2; Diao, Zulong 5; Liang, Wei 4; Xie, Gaogang 6,7; Cao, Jiannong 8
	2024-11-01
发表期刊	IEEE TRANSACTIONS ON SERVICES COMPUTING
ISSN	1939-1374
卷号	17 期号:6 页码:3713-3726
摘要	Accurately identifying IoT device types is crucial for IoT security and resource management. However, existing traffic-based device identification algorithms incur high measurement, storage, and computation costs, as they continuously need to capture, store, and parse device traffic. To overcome these challenges, we propose an innovative framework that employs a discontinuous traffic measurement strategy, reducing the number of packets captured, stored, and parsed. To ensure accurate identification, we introduce several novel techniques. First, we propose a graph neural network-based tensor completion model to estimate missing traffic features in unmeasured time slots. Our model can utilize historical information to flexibly and efficiently estimate missing features. Second, we propose a convolutional neural network-based classifier for device identification. The classifier utilizes traffic features and node embeddings learned from the tensor completion model to achieve precise device identification. Through extensive experiments on real IoT traffic traces, we demonstrate that our framework achieves high accuracy while significantly reducing costs. For instance, by capturing only 30% of the packets, our framework can identify devices with a high accuracy of 0.9558. Moreover, compared to current tensor completion methods, our method can estimate missing values with higher accuracy and achieve a 1.53-fold speedup over the next-fastest baseline.
关键词	Object recognition Internet of Things Tensors Feature extraction Accuracy Logic gates Data models Internet of Things (IoT) device-type identification tensor completion graph neural networks
DOI	10.1109/TSC.2024.3463496
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62025201] ; National Natural Science Foundation of China[62472167] ; Hunan Provincial Natural Science Foundation of China[2024JJ3014] ; Hunan Provincial Natural Science Foundation of China[2024JJ5165] ; Key Research and Development Program of Hunan Province[2023GK2001]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering
WOS记录号	WOS:001386516500043
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40799
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xie, Kun; Wen, Jigang
作者单位	1.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China 2.Minist Educ Key Lab Fus Comp Supercomp & Artificia, Changsha 410082, Peoples R China 3.SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA 4.Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan 411201, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China 6.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China 7.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100190, Peoples R China 8.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Wang, Haoxuan,Xie, Kun,Wang, Xin,et al. GDI: A Novel IoT Device Identification Framework via Graph Neural Network-Based Tensor Completion[J]. IEEE TRANSACTIONS ON SERVICES COMPUTING,2024,17(6):3713-3726.
APA	Wang, Haoxuan.,Xie, Kun.,Wang, Xin.,Wen, Jigang.,Xie, Ruotian.,...&Cao, Jiannong.(2024).GDI: A Novel IoT Device Identification Framework via Graph Neural Network-Based Tensor Completion.IEEE TRANSACTIONS ON SERVICES COMPUTING,17(6),3713-3726.
MLA	Wang, Haoxuan,et al."GDI: A Novel IoT Device Identification Framework via Graph Neural Network-Based Tensor Completion".IEEE TRANSACTIONS ON SERVICES COMPUTING 17.6(2024):3713-3726.