随着互联网技术的不断发展, 网络的安全问题日益受到人们的重视. 网络异常流量检测能够为拦截网络攻击提供有效的保障. 然而, 为了准确检测网络中的异常流量, 通常需要分析海量的数据. 分析这些数据不仅消耗巨大的计算资源, 降低检测的实时性, 还有可能降低检测的准确率. 为解决这些问题, 提出了一种基于集成特征选择的网络异常流量检测方法: 采用5种不同的特征选择算法, 设计了一种投票机制以选择特征子集; 用朴素贝叶斯、决策树、XGBoost (eXtreme Gradient Boosting)这3种不同的机器学习算法, 评估所采用的特征选择算法; 选择表现最好的算法以实现网络异常流量检测. 实验结果表明, 在使用提出的算法所选取出的最优子特征上, 所提方法的运行时间比在原始数据集上少了84.38%, 平均准确率比单个特征选择算法提高了16.93%.

With the continuous development of Internet technology, network security is garnering increasing attention. Network anomalous traffic detection can provide an effective guarantee for blocking network attacks. However, to accurately detect anomalous traffic in a network, analyzing large volumes of data is usually required. Analyzing this data not only consumes substantial computational resources and reduces real-time detection capability, but it may also reduce the overall accuracy of detection. To solve these problems, we propose a network anomaly traffic detection method based on ensemble feature selection. Specifically, we use five different feature selection algorithms to design a voting mechanism for selecting feature subsets. Three different machine learning algorithms (Naive Bayesian, Decision Tree, XGBoost) are used to evaluate the feature selection algorithm, and the best algorithm is selected to detect abnormal network traffic. The experimental results show that the runtime of the proposed method is 84.38% less than the original data set on the optimal feature subset selected by the proposed approach, and the average accuracy is 16.93% higher than that of the single feature selection algorithm.