Chen Chen, Yajiang Qi, Xiaoyan Ye, Guanghua Wang, Lintao Yang, Haiyue Ji
{"title":"基于异构基础学习器的信息安全入侵检测的堆叠集成学习模型","authors":"Chen Chen, Yajiang Qi, Xiaoyan Ye, Guanghua Wang, Lintao Yang, Haiyue Ji","doi":"10.1117/12.2653422","DOIUrl":null,"url":null,"abstract":"In network intrusion detection, using a machine learning method alone has blind spots and low detection accuracy. A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection is proposed. Firstly, the convolution neural network is used to extract the deep information in the original data set, which is normalized as the input of the model. In constructing base classifiers, different heterogeneous model combinations are used to enhance the diversity of base classifiers. Experiments on NSL-KDD dataset show that the proposed model can comprehensively improve the detection accuracy, accuracy, recall and F1-score.","PeriodicalId":32903,"journal":{"name":"JITeCS Journal of Information Technology and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection\",\"authors\":\"Chen Chen, Yajiang Qi, Xiaoyan Ye, Guanghua Wang, Lintao Yang, Haiyue Ji\",\"doi\":\"10.1117/12.2653422\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In network intrusion detection, using a machine learning method alone has blind spots and low detection accuracy. A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection is proposed. Firstly, the convolution neural network is used to extract the deep information in the original data set, which is normalized as the input of the model. In constructing base classifiers, different heterogeneous model combinations are used to enhance the diversity of base classifiers. Experiments on NSL-KDD dataset show that the proposed model can comprehensively improve the detection accuracy, accuracy, recall and F1-score.\",\"PeriodicalId\":32903,\"journal\":{\"name\":\"JITeCS Journal of Information Technology and Computer Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JITeCS Journal of Information Technology and Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2653422\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JITeCS Journal of Information Technology and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2653422","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection
In network intrusion detection, using a machine learning method alone has blind spots and low detection accuracy. A stacked ensemble learning model using heterogeneous base-leaners for information security intrusion detection is proposed. Firstly, the convolution neural network is used to extract the deep information in the original data set, which is normalized as the input of the model. In constructing base classifiers, different heterogeneous model combinations are used to enhance the diversity of base classifiers. Experiments on NSL-KDD dataset show that the proposed model can comprehensively improve the detection accuracy, accuracy, recall and F1-score.