{"title":"基于boost算法的入侵检测特征选择与实现","authors":"Utpal Shrivastava, Neelam Sharma","doi":"10.1109/ComPE49325.2020.9200072","DOIUrl":null,"url":null,"abstract":"Monitoring of the data traffic is done by Intrusion detection system (IDS) in the network and identify possibility of attacks with can cause harm in the network. The growing digital age where so many host are connected to network and digital transaction take place, it becomes important to secure one’s data in the network. In the proposed work, NSL-KDD train dataset in ration 8:2 is used to train and test a model. To identify the impact of different set of dataset features considered by comparing the accuracy calculated.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"25 1","pages":"853-858"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature Selection and Implementation of IDS using Boosting algorithm\",\"authors\":\"Utpal Shrivastava, Neelam Sharma\",\"doi\":\"10.1109/ComPE49325.2020.9200072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Monitoring of the data traffic is done by Intrusion detection system (IDS) in the network and identify possibility of attacks with can cause harm in the network. The growing digital age where so many host are connected to network and digital transaction take place, it becomes important to secure one’s data in the network. In the proposed work, NSL-KDD train dataset in ration 8:2 is used to train and test a model. To identify the impact of different set of dataset features considered by comparing the accuracy calculated.\",\"PeriodicalId\":6804,\"journal\":{\"name\":\"2020 International Conference on Computational Performance Evaluation (ComPE)\",\"volume\":\"25 1\",\"pages\":\"853-858\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Computational Performance Evaluation (ComPE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ComPE49325.2020.9200072\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Computational Performance Evaluation (ComPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ComPE49325.2020.9200072","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feature Selection and Implementation of IDS using Boosting algorithm
Monitoring of the data traffic is done by Intrusion detection system (IDS) in the network and identify possibility of attacks with can cause harm in the network. The growing digital age where so many host are connected to network and digital transaction take place, it becomes important to secure one’s data in the network. In the proposed work, NSL-KDD train dataset in ration 8:2 is used to train and test a model. To identify the impact of different set of dataset features considered by comparing the accuracy calculated.
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