Intelligent device recognition of internet of things based on machine learning

• Published in: Intelligent systems with applications Vol. 22; p. 200368
• Main Author: Huang, Sheng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-06-2024; Elsevier
• Subjects: Communication behavior monitoring; Fingerprint identification; Internet of Things technology; LSTM neural network; NSGA-III algorithm; Communication behavior monitoring; Internet of Things technology; Fingerprint identification; NSGA-III algorithm; LSTM neural network
• ISSN: 2667-3053; 2667-3053
• DOI: 10.1016/j.iswa.2024.200368

•High accuracy rates: the research results demonstrate impressive accuracy rates, with 99.9 % accuracy achieved at 30-minute and 60-minute intervals. The method outperformed the baseline by improving average accuracy by 1.5 % in 100 experiments.•Long short-term memory network for fingerprint recognition: the study achieved over 90 % accuracy in fingerprint recognition, with a high area under the curve of 0.99. Most devices had a recognition accuracy of over 95 %, and the recall rate remained consistently around 90 %.•Implications for network security: the proposed method not only enhances device recognition accuracy and efficiency but also provides valuable solutions for network security. The research findings offer practical guidance and applications in related fields.
With the rapid popularization and application of Internet of Things technology, smart devices have become an indispensable part of people's daily lives. Therefore, it is crucial to accurately identify these devices as their numbers continue to grow. The research aimed to introduce a lightweight method for identifying Internet of Things devices based on network flow characteristics and scheduling algorithms. This can improve device identification accuracy while maintaining high efficiency. The research constructed a comprehensive optimization algorithm selection framework to achieve performance optimization in different scenarios. This framework took into account many factors, including network traffic characteristics, device identification requirements, and system efficiency, to ensure flexible adaptation in different scenarios and optimize overall performance. Research results showed that the proposed system had an accuracy of 96.8 % at 1-minute intervals, which increased to 99.7 % at 10-minute intervals, and reached 99.9 % at both 30-minute and 60-minute intervals. In 100 experiments, the research method improved the accuracy by an average of 1.5 % compared with the baseline. In fingerprint recognition, the overall accuracy of the long short-term memory network exceeded 90 %, with an area under the curve of 0.99. Most devices had an accuracy of over 95 % in recognition, and the recall rate remained around 90 %, the effectiveness of the method proposed in the study was further verified. The method proposed in the study not only improved the accuracy and efficiency of device recognition, but also provided powerful solutions for the field of network security. This provides useful guidance for research and practical applications in related fields.