基于平均特征重要性和集成学习的异常检测  

Anomaly Detection Based on Average Feature Importance and Ensemble Learning

作　　者：庄锐 张浩[1,2] ZHUANG Rui;ZHANG Hao(College of Computer and Data Science,Fuzhou University,Fuzhou 350116,China;Fujian Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou 350116,China)

机构地区：[1]福州大学计算机与大数据学院,福州350116 [2]福建省网络计算与智能信息处理重点实验室,福州350116

出　　处：《计算机系统应用》2023年第6期60-69,共10页Computer Systems & Applications

基　　金：国家自然科学基金重点项目(U1804263,U21A20472);国家留学基金青年骨干教师出国研修项目;福建省自然科学基金(2021J01616,2020J01130167,2021J01625)。

摘　　要：异常检测系统在网络空间安全中起着至关重要的作用,为网络安全提供有效的保障.对于复杂的网络流量信息,传统的单一的分类器往往无法同时具备较高检测精确度和较强的泛化能力.此外,基于全特征的异常检测模型往往会受到冗余特征的干扰,影响检测的效率和精度.针对这些问题,本文提出了一种基于平均特征重要性的特征选择和集成学习的模型,选取决策树(DT)、随机森林(RF)、额外树(ET)作为基分类器,建立投票集成模型,并基于基尼系数计算基分类器的平均特征重要性进行特征选择.在多个数据集上的实验评估结果表明,本文提出的集成模型优于经典集成学习模型及其他著名异常检测集成模型.且提出的基于平均特征重要性的特征选择方法可以使集成模型准确率平均进一步提升约0.13%,训练时间平均节省约30%.Anomaly detection system plays a significant role in cyberspace security and provides effective protection for network security.Regarding complex network traffic information,the traditional single classifier is often unable to ensure high detection accuracy and strong generalization ability at the same time.In addition,the anomaly detection model based on full features is often disturbed by redundancy features,which affects the accuracy and efficiency of detection.To address these problems,this study proposes a feature selection and ensemble learning model based on average feature importance.The decision tree(DT),random forest(RF),and extra tree(ET)are selected as the base classifiers to establish a voting ensemble model,and the average feature importance of the base classifiers is calculated based on the Gini coefficient for feature selection.The experimental evaluation results on several datasets show that the proposed model is superior to the classical ensemble learning models and other well-known anomaly detection ensemble models.The proposed model can improve the accuracy of the ensemble model by about 0.13%and save about 30%of training time on average.

关 键 词：网络入侵检测 异常流量 特征选择 集成学习 异常检测 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]