二维码（扫一下试试看！） | | 基于SMOTE和机器学习的网络入侵检测 | | Research on Network Intrusion Detection Based on SMOTE Algorithm and Machine Learning | | 投稿时间：2018-11-02 | | DOI：10.15918/j.tbit1001-0645.2018.423 | | 中文关键词:网络入侵检测SMOTE算法机器学习数据再平衡 | | English Keywords:network intrusion detectionSMOTE algorithmmachine learningdata rebalancing | | 基金项目: | | | 摘要点击次数:737 | | 全文下载次数:580 | | 中文摘要: | | 网络入侵检测已经广泛运用机器学习模型，但是研究者们多关注模型选择和参数优化，很少考虑数据不平衡的影响，往往会导致少数类入侵样本的检测效果较差.针对该问题，以SMOTE （synthetic minority oversampling technique）数据再平衡算法为研究重点，应用入侵检测数据集KDD99作为原始训练集，使用简单抽样和SMOTE算法生成再平衡训练集.采用多种机器学习模型分别在原始训练集和再平衡训练集进行5折交叉验证.实验结果表明，与原始训练集相比，使用再平衡训练集建模能够在不降低甚至提高多数类样本识别效果前提下，使少数类样本的识别准确率和召回率增强10%～20%.因此，SMOTE算法对不平衡样本下的网络入侵检测有显著的提升作用. | | English Summary: | | The machine learning model has been widely used in network intrusion detection, but researchers pay more attention to model selection and parameter optimization, but rarely consider the impact of data imbalance, which often leads to poor detection effect of a small number of intrusion samples. To solve this problem, focusing on the data rebalancing algorithm of SMOTE(synthetic minority oversampling technique), taking the intrusion detection data set KDD99 as the original training set,a simple sampling method and SMOTE algorithm were used to generate the rebalancing training set. And then,a variety of machine learning models were used to perform 5 fold cross-validation for the original training set and the rebalanced training set respectively. Experimental results show that, compared with the original training set, the use of rebalancing training set modeling can improve the recognition accuracy and recall rate of the minor class samples by about 10%~20% without reducing or even improving the recognition effect of major class samples. Therefore, SMOTE algorithm can significantly improve network intrusion detection under unbalanced samples. | | 查看全文查看/发表评论下载PDF阅读器 | |
罗玉文,柳丹,范雄华,江晶.空地概率数据关联转换量测滤波算法[J].北京理工大学学报（自然科学版）,2019,39(12):1268~1276.LUOYu-wen,LIUDan,FANXiong-hua,JIANGJing.StudyonAir-GroundProbabilityDataAssoci ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21韩先君,刘艳丽,杨红雨.基于生成对抗网络的人脸图像彩色化方法[J].北京理工大学学报（自然科学版）,2019,39(12):1285~1291.HANXian-jun,LIUYan-li,YANGHong-yu.FaceImageColorizationBasedonGenerativeAdvers ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21.北京理工大学学报2019年总目次（第39卷）[J].北京理工大学学报（自然科学版）,2019,39(12):1321~1338..[J].TransactionsofBeijingInstituteofTechnology,2019,39(12):1321-1338.二维码（扫一下试试看！）北京理 ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21李金,刘文杰,孟祥莲,边晨源,梁洪.脑影像与基因组数据关联的交互可视化方法研究[J].北京理工大学学报（自然科学版）,2019,39(S1):12~18.LIJin,LIUWen-jie,MENGXiang-lian,BIANChen-yuan,LIANGHong.ResearchonInterac ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21李金,陈凤,孟祥莲,边晨源,梁洪.脑脊液中t-tau蛋白的全基因组网络辅助分析和富集研究[J].北京理工大学学报（自然科学版）,2019,39(S1):6~11.LIJin,CHENFeng,MENGXiang-lian,BIANChen-yuan,LIANGHong.Genome-WideNetw ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21庞枫骞,刘志文,时永刚.基于生成对抗网络的细胞形变动态分类[J].北京理工大学学报（自然科学版）,2019,39(S1):33~37.PANGFeng-qian,LIUZhi-wen,SHIYong-gang.ClassificationofCellDeformationDynamicsBasedo ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21程坤,时永刚,李依桐,刘志文.基于生成对抗网络的海马子区图像分割[J].北京理工大学学报（自然科学版）,2019,39(S1):159~163.CHENGKun,SHIYong-gang,LIYi-tong,LIUZhi-wen.ImageSegmentationofHippocampalSubfi ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21王征晨,王兴华,仲顺安.90nmCMOS工艺高速锁相环设计与优化[J].北京理工大学学报（自然科学版）,2018,38(1):58~62.WANGZheng-chen,WANGXing-hua,ZHONGShun-an.DesignandOptimizationofHighSpeedPLLBased ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21严阳阳,刘斌,王士伟,卢威,丁英涛.脉冲无氰电镀在硅基RF-MEMS滤波器中的应用及优化[J].北京理工大学学报（自然科学版）,2018,38(1):68~72.YANYang-yang,LIUBin,WANGShi-wei,LUWei,DINGYing-tao.OptimizationonNon- ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21张希,缪玲娟,沈军,邵海俊.GPS/INS超紧组合导航滤波器的优化设计[J].北京理工大学学报（自然科学版）,2018,38(1):102~107.ZHANGXi,MIAOLing-juan,SHENJun,SHAOHai-jun.OptimizedDesignoftheNavigationFilt ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21
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