Power Internet of Things traffic classification system based on reinforcement learning

Qi Wang; Fei Liang; Yang He; Peng Gao; Qigui Yao

doi:10.1117/12.2683573

26 June 2023 Power Internet of Things traffic classification system based on reinforcement learning

Qi Wang, Fei Liang, Yang He, Peng Gao, Qigui Yao

Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 127210P (2023) https://doi.org/10.1117/12.2683573
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China

Abstract

Network traffic classification technology is an important means for power Internet of things to carry out network management and maintain network security. However, there are many existing traffic classification methods. Different traffic classification methods face different data sets, and the data sets used for training are limited, the update is slow, and the change of traffic characteristics is not obvious. Therefore, based on passive detection technology, this paper uses traffic analysis as a tool to collect the lossless traffic data of the target network, and then uses reinforcement learning Q-learning algorithm to classify the traffic and design the corresponding return function, and adopt ε-greedy exploration strategy and delayed return strategy to improve the learning effect of agents and improve the accuracy and efficiency of classification to a greater extent. Finally, the feasibility of the system is verified by experimental simulation. After 100 days of training, the classification accuracy has exceeded 85%, and with the increase of training time, the classification accuracy will be further improved.

Citation Download Citation

Qi Wang, Fei Liang, Yang He, Peng Gao, and Qigui Yao "Power Internet of Things traffic classification system based on reinforcement learning", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 127210P (26 June 2023); https://doi.org/10.1117/12.2683573

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KEYWORDS

Machine learning

Classification systems

Education and training

Data acquisition

Internet of things

Deep learning

Design and modelling

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