{"title":"一种网络IDS告警分类的流聚类算法","authors":"Risto Vaarandi","doi":"10.1109/CSR51186.2021.9527926","DOIUrl":null,"url":null,"abstract":"Network IDS is a widely used security monitoring technology for detecting cyber attacks, malware activity, and other unwanted network traffic. Unfortunately, network IDSs are known to generate a large number of alerts which overwhelm the human analyst, with many alerts having low importance or being false positives. This paper addresses this issue and proposes a lightweight stream clustering algorithm for classifying IDS alerts and discovering frequent attack scenarios.","PeriodicalId":253300,"journal":{"name":"2021 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Stream Clustering Algorithm for Classifying Network IDS Alerts\",\"authors\":\"Risto Vaarandi\",\"doi\":\"10.1109/CSR51186.2021.9527926\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Network IDS is a widely used security monitoring technology for detecting cyber attacks, malware activity, and other unwanted network traffic. Unfortunately, network IDSs are known to generate a large number of alerts which overwhelm the human analyst, with many alerts having low importance or being false positives. This paper addresses this issue and proposes a lightweight stream clustering algorithm for classifying IDS alerts and discovering frequent attack scenarios.\",\"PeriodicalId\":253300,\"journal\":{\"name\":\"2021 IEEE International Conference on Cyber Security and Resilience (CSR)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Cyber Security and Resilience (CSR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSR51186.2021.9527926\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Cyber Security and Resilience (CSR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSR51186.2021.9527926","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Stream Clustering Algorithm for Classifying Network IDS Alerts
Network IDS is a widely used security monitoring technology for detecting cyber attacks, malware activity, and other unwanted network traffic. Unfortunately, network IDSs are known to generate a large number of alerts which overwhelm the human analyst, with many alerts having low importance or being false positives. This paper addresses this issue and proposes a lightweight stream clustering algorithm for classifying IDS alerts and discovering frequent attack scenarios.
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