Research Article
Identification of IoT Devices Based on Probability Adaptive Boosting
@INPROCEEDINGS{10.1007/978-3-032-33662-0_42, author={Jian Du and Linjia He and Ming Dai and Jie Luo and Tao Yang and Hongwei Xing and Qingqing Chu}, title={Identification of IoT Devices Based on Probability Adaptive Boosting}, proceedings={Communications and Networking. 20th EAI International Conference, ChinaCom 2025, Xi’an, China, November 8--9, 2025, Proceedings}, proceedings_a={CHINACOM}, year={2026}, month={10}, keywords={Internet of Things Device Identification Scanning Techniques Machine Learning}, doi={10.1007/978-3-032-33662-0_42} }- Jian Du
Linjia He
Ming Dai
Jie Luo
Tao Yang
Hongwei Xing
Qingqing Chu
Year: 2026
Identification of IoT Devices Based on Probability Adaptive Boosting
CHINACOM
Springer
DOI: 10.1007/978-3-032-33662-0_42
Abstract
With the rapid development of the Internet of Things (IoT), device identification has become a critical research direction for IoT security. Traditional two-stage device identification methods based on Internet fingerprints suffer from low identification accuracy, high model redundancy, and significant time complexity. To address these issues, this paper proposes a method for IoT device identification based on probability adaptive boosting, aimed at improving identification accuracy and reducing time complexity. This method constructs new fingerprint features by analyzing network protocol fields and traffic behavior patterns, employing the adaptive boosting algorithm to learn traffic behavior patterns, and incorporating traffic perturbations to enhance performance. Experimental results demonstrate that the proposed method significantly improves the accuracy and efficiency of device identification, holding practical application value and better meeting the device detection demands in the current network environment.