基于随机森林的物联网设备流量分类算法

The traffic classification of Internet of things (IoT) devices is very important to the management of cyberspace assets. The classification technology based on statistical identification is a hot spot in current academic research. The previous algorithms were mainly based on the flow information to set up the feature vectors, but lesson the packet information. In this paper, we improve the traffic classification algorithm of IoT devices based on random forest. We set up the feature vectors with both the flow information and the flow's packet information. The experimental results show that, compared with previous algorithms, the classification accuracy of the proposed algorithm increases from 56% to 82%, the recall rate improves from 47% to 67%, the F₁ score increases from 0.43 to 0.74, and the confusion matrix correlation is also significantly improved. As a result, the proposed algorithm has a better classification effect than previous ones.