GraphIoT: Lightweight IoT Device Detection Based on Graph Classifiers and Incremental Learning

doi:10.1109/TSC.2024.3466854

	GraphIoT: Lightweight IoT Device Detection Based on Graph Classifiers and Incremental Learning
	Yin, Yansong 1,2; Xie, Kun 1,2; He, Shiming 3; Li, Yanbiao 4,5; Wen, Jigang 6; Diao, Zulong 7,8; Zhang, Dafang 1; Xie, Gaogang 4,5
	2024-11-01
发表期刊	IEEE TRANSACTIONS ON SERVICES COMPUTING
ISSN	1939-1374
卷号	17 期号:6 页码:3758-3772
摘要	The rapid expansion of the Internet of Things (IoT) has led to growing concerns about the security of IoT devices. A crucial aspect of ensuring their security is IoT device identification, which involves pinpointing the specific type of device. Existing solutions, however, either necessitate complex feature engineering or struggle to handle the ever-increasing number of new devices in open IoT environments. To tackle these challenges, this paper introduces GraphIoT, a lightweight IoT device detection method based on graph classifiers. GraphIoT leverages lightweight flow information, such as packet length, direction, and timestamp, to create an IoT Device Traffic Graph Representation (IoT-DTGR). This representation offers a comprehensive view of IoT device flows while preserving features in bidirectional IoT Device-Gateway interactions. By transforming the IoT device detection problem into a graph classification problem, GraphIoT employs a powerful Graph Neural Network that takes into account both node and edge features, as well as subgraph structures in IoT-DTGRs, to classify graphs and consequently identify device types. Additionally, the paper proposes an incremental learning framework called CL-GraphIoT that continuously learns features of new IoT device flows without forgetting previously learned device features. This is achieved through two strategies: parameter sharing and sample replaying. The paper gathers a real-world dataset from 18 IoT devices and conducts experiments on two datasets: the gathered real-world dataset and an open-source dataset covering 21 IoT device types. The experimental results demonstrate that both GraphIoT and CL-GraphIoT outperform state-of-the-art methods, achieving high accuracy in device detection with fast processing speed.
关键词	Internet of Things Logic gates Feature extraction Protocols Learning systems Object recognition IP networks Graph neural networks incremental learning IoT device detection
DOI	10.1109/TSC.2024.3466854
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[62321166652] ; National Natural Science Foundation of China[62472167] ; National Natural Science Foundation of China[62272062] ; Hunan Provincial Natural Science Foundation of China[2024]J3014] ; Hunan Provincial Natural Science Foundation of China[2024]J5165] ; Science and Technology Innovation Program of Hunan Province[2023RC3139] ; 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:001386516500032
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/2XEOYT63/40807
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
作者单位	1.Hunan Univ, Coll Comp Sci, Elect Engn, Changsha 410012, Hunan, Peoples R China 2.Minist Educ, Key Lab Fus Comp Supercomputing Artificial Intelli, Guangzhou 510000, Peoples R China 3.Changsha Univ Sci & Technol, Sch Comp Sci, Commun Engn, Changsha 410205, Hunan, Peoples R China 4.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100045, Peoples R China 5.Univ Chinese Acad Sci, Sch Comp Sci Technol, Beijing 101408, Peoples R China 6.Hunan Univ Sci Technol, Sch Comp Sci, Engn, Xiangtan 411199, Peoples R China 7.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China 8.Purple Mt Labs, Nanjing 210023, Peoples R China
推荐引用方式 GB/T 7714	Yin, Yansong,Xie, Kun,He, Shiming,et al. GraphIoT: Lightweight IoT Device Detection Based on Graph Classifiers and Incremental Learning[J]. IEEE TRANSACTIONS ON SERVICES COMPUTING,2024,17(6):3758-3772.
APA	Yin, Yansong.,Xie, Kun.,He, Shiming.,Li, Yanbiao.,Wen, Jigang.,...&Xie, Gaogang.(2024).GraphIoT: Lightweight IoT Device Detection Based on Graph Classifiers and Incremental Learning.IEEE TRANSACTIONS ON SERVICES COMPUTING,17(6),3758-3772.
MLA	Yin, Yansong,et al."GraphIoT: Lightweight IoT Device Detection Based on Graph Classifiers and Incremental Learning".IEEE TRANSACTIONS ON SERVICES COMPUTING 17.6(2024):3758-3772.