{"title":"基于马尔可夫链模型的异常检测","authors":"Michael Zheludev, Evgeny Nagradov","doi":"10.1109/CSITECHNOL.2017.8312166","DOIUrl":null,"url":null,"abstract":"This paper provides a method of mathematical representation of the traffic flow of network states. The flow of states is represented as transitions to the Markov Chains. Anomalies are interpreted as graph transitions with low probabilities.","PeriodicalId":332371,"journal":{"name":"2017 Computer Science and Information Technologies (CSIT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Anomaly detection using Markov chain model\",\"authors\":\"Michael Zheludev, Evgeny Nagradov\",\"doi\":\"10.1109/CSITECHNOL.2017.8312166\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper provides a method of mathematical representation of the traffic flow of network states. The flow of states is represented as transitions to the Markov Chains. Anomalies are interpreted as graph transitions with low probabilities.\",\"PeriodicalId\":332371,\"journal\":{\"name\":\"2017 Computer Science and Information Technologies (CSIT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Computer Science and Information Technologies (CSIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSITECHNOL.2017.8312166\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Computer Science and Information Technologies (CSIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSITECHNOL.2017.8312166","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper provides a method of mathematical representation of the traffic flow of network states. The flow of states is represented as transitions to the Markov Chains. Anomalies are interpreted as graph transitions with low probabilities.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
