{"title":"入侵检测系统的递归特征消除算法","authors":"H. Nguyen","doi":"10.1109/ICBIR54589.2022.9786411","DOIUrl":null,"url":null,"abstract":"Identification of the attacks on computer networks has significant importance in security research. In this paper, a set of machine learning algorithms are investigated for the design of intrusion detection systems. The detection performance of the proposed intrusion detection system is improved by the application of the recursive feature elimination method to rank the entire input features and generate various feature combinations based on their importance. The crossvalidation procedure is also implemented for the machine learning techniques on the validation dataset using a larger number of feature combinations. The high classification performance of the proposed algorithm for the intrusion detection system implies a better capability of application in the practical environment of computer networks.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recursive Feature Elimination Algorithm for Intrusion Detection Systems\",\"authors\":\"H. Nguyen\",\"doi\":\"10.1109/ICBIR54589.2022.9786411\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Identification of the attacks on computer networks has significant importance in security research. In this paper, a set of machine learning algorithms are investigated for the design of intrusion detection systems. The detection performance of the proposed intrusion detection system is improved by the application of the recursive feature elimination method to rank the entire input features and generate various feature combinations based on their importance. The crossvalidation procedure is also implemented for the machine learning techniques on the validation dataset using a larger number of feature combinations. The high classification performance of the proposed algorithm for the intrusion detection system implies a better capability of application in the practical environment of computer networks.\",\"PeriodicalId\":216904,\"journal\":{\"name\":\"2022 7th International Conference on Business and Industrial Research (ICBIR)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 7th International Conference on Business and Industrial Research (ICBIR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICBIR54589.2022.9786411\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Business and Industrial Research (ICBIR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBIR54589.2022.9786411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recursive Feature Elimination Algorithm for Intrusion Detection Systems
Identification of the attacks on computer networks has significant importance in security research. In this paper, a set of machine learning algorithms are investigated for the design of intrusion detection systems. The detection performance of the proposed intrusion detection system is improved by the application of the recursive feature elimination method to rank the entire input features and generate various feature combinations based on their importance. The crossvalidation procedure is also implemented for the machine learning techniques on the validation dataset using a larger number of feature combinations. The high classification performance of the proposed algorithm for the intrusion detection system implies a better capability of application in the practical environment of computer networks.