Power Internet of Things traffic classification system based on reinforcement learning

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 Qlearning algorithm to classify the traffic and design the corresponding return function, and adopt  -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.