Samuel Marchal, J. François, C. Wagner, R. State, A. Dulaunoy, T. Engel, O. Festor
{"title":"dssm:大规模被动DNS安全监控框架","authors":"Samuel Marchal, J. François, C. Wagner, R. State, A. Dulaunoy, T. Engel, O. Festor","doi":"10.1109/NOMS.2012.6212019","DOIUrl":null,"url":null,"abstract":"We present a monitoring approach and the supporting software architecture for passive DNS traffic. Monitoring DNS traffic can reveal essential network and system level activity profiles. Worm infected and botnet participating hosts can be identified and malicious backdoor communications can be detected. Any passive DNS monitoring solution needs to address several challenges that range from architectural approaches for dealing with large volumes of data up to specific Data Mining approaches for this purpose. We describe a framework that leverages state of the art distributed processing facilities with clustering techniques in order to detect anomalies in both online and offline DNS traffic. This framework entitled DNSSM is implemented and operational on several networks. We validate the framework against two large trace sets.","PeriodicalId":364494,"journal":{"name":"2012 IEEE Network Operations and Management Symposium","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"36","resultStr":"{\"title\":\"DNSSM: A large scale passive DNS security monitoring framework\",\"authors\":\"Samuel Marchal, J. François, C. Wagner, R. State, A. Dulaunoy, T. Engel, O. Festor\",\"doi\":\"10.1109/NOMS.2012.6212019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a monitoring approach and the supporting software architecture for passive DNS traffic. Monitoring DNS traffic can reveal essential network and system level activity profiles. Worm infected and botnet participating hosts can be identified and malicious backdoor communications can be detected. Any passive DNS monitoring solution needs to address several challenges that range from architectural approaches for dealing with large volumes of data up to specific Data Mining approaches for this purpose. We describe a framework that leverages state of the art distributed processing facilities with clustering techniques in order to detect anomalies in both online and offline DNS traffic. This framework entitled DNSSM is implemented and operational on several networks. We validate the framework against two large trace sets.\",\"PeriodicalId\":364494,\"journal\":{\"name\":\"2012 IEEE Network Operations and Management Symposium\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"36\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Network Operations and Management Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NOMS.2012.6212019\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Network Operations and Management Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NOMS.2012.6212019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DNSSM: A large scale passive DNS security monitoring framework
We present a monitoring approach and the supporting software architecture for passive DNS traffic. Monitoring DNS traffic can reveal essential network and system level activity profiles. Worm infected and botnet participating hosts can be identified and malicious backdoor communications can be detected. Any passive DNS monitoring solution needs to address several challenges that range from architectural approaches for dealing with large volumes of data up to specific Data Mining approaches for this purpose. We describe a framework that leverages state of the art distributed processing facilities with clustering techniques in order to detect anomalies in both online and offline DNS traffic. This framework entitled DNSSM is implemented and operational on several networks. We validate the framework against two large trace sets.