Research on Automated Classification Method of Network Attacking Based on Gradient Boosting Decision Tree
Renhao Wen, Kaiyue Zhang · 2022
With the continuous expansion of network scale and the continuous development of various network applications, various network attacks are becoming more and more rampant, bringing huge potential risks to network security. This paper is based on the method of the neural network to study the method of solving the above problems. The main research work and innovation of this paper are to detect network anomaly traffic and classify different types of network attacks by using a multilayer neural network. The core of the intrusion detection module based on a multilayer neural network is the intrusion detection algorithm based on a multilayer neural network. The classification algorithm mainly uses random forest, Kmeans, and Gradient Boosting Decision Tree. In this paper, a variety of machine learning algorithms such as Random Forest, Kmeans, and Gradient Boosting Decision Tree are used to construct the classification learner. The model is evaluated using public data sets in real business scenarios. Experimental results show that the model constructed by Gradient Boosting Decision Tree has a more accurate and efficient effect in network anomaly monitoring, and the granularity of network attack types is more refined and accurate. It can effectively and actively target network attacks, reduce the noise of different attribute characteristic domains in network traffic, eliminate the correlation between them, improve the detection rate, correctness, and accuracy of attack detection, and provide better network security defense for the field of big data.