关键词: 网络安全, 机器学习, 随机森林方法, 攻击检测

Abstract: In order to improve the level of network security and detect network attack actively in time, this paper proposes a network intrusion detection model based on random forest. The model can perform distributed detection on large traffic attacks, and after introducing two randomness, the detection algorithm can reduce the noise of different attribute feature fields in network traffic and eliminate the correlation, so that it is more convenient and rapid to actively detect attack. Finally, the classical Adaboost combinatorial multi-classifier method is compared with the algorithm proposed in this paper in three aspects：detection rate, accuracy rate and precision rate, which reflects the advantages of this algorithm and provides better protection for network security in the era of big data.

Key words: network security, machine learning, random forest method, attack detect