{"title":"检测组织专用网络中的异常网络流量","authors":"Risto Vaarandi","doi":"10.1109/COGSIMA.2013.6523859","DOIUrl":null,"url":null,"abstract":"During the last decade, network monitoring and intrusion detection have become essential techniques of cyber security. Nowadays, many institutions are using advanced solutions for detecting malicious network traffic, discovering network anomalies, and preventing cyber attacks. However, most research in this area has not been conducted specifically for organizational private networks, and their special properties have not been considered. In this paper, we first present a study of traffic patterns in a corporate private network, and then propose two novel algorithms for detecting anomalous network traffic and node behavior in such networks.","PeriodicalId":243766,"journal":{"name":"2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Detecting anomalous network traffic in organizational private networks\",\"authors\":\"Risto Vaarandi\",\"doi\":\"10.1109/COGSIMA.2013.6523859\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"During the last decade, network monitoring and intrusion detection have become essential techniques of cyber security. Nowadays, many institutions are using advanced solutions for detecting malicious network traffic, discovering network anomalies, and preventing cyber attacks. However, most research in this area has not been conducted specifically for organizational private networks, and their special properties have not been considered. In this paper, we first present a study of traffic patterns in a corporate private network, and then propose two novel algorithms for detecting anomalous network traffic and node behavior in such networks.\",\"PeriodicalId\":243766,\"journal\":{\"name\":\"2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COGSIMA.2013.6523859\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COGSIMA.2013.6523859","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detecting anomalous network traffic in organizational private networks
During the last decade, network monitoring and intrusion detection have become essential techniques of cyber security. Nowadays, many institutions are using advanced solutions for detecting malicious network traffic, discovering network anomalies, and preventing cyber attacks. However, most research in this area has not been conducted specifically for organizational private networks, and their special properties have not been considered. In this paper, we first present a study of traffic patterns in a corporate private network, and then propose two novel algorithms for detecting anomalous network traffic and node behavior in such networks.