{"title":"一种针对异常对策的规模化免疫方法:将pH与cfengine相结合","authors":"Kyrre M. Begnum, M. Burgess","doi":"10.1109/INM.2003.1194158","DOIUrl":null,"url":null,"abstract":"We discuss the combination of two anomaly detection models, the Linux kernel module pH and cfengine, in order to create a multi-scaled approach to computer anomaly detection with automated response. By examining the time-average data from pH, we find the two systems to be conceptually complementary and to have compatible data models. Based on these findings, we build a simple prototype system and comment on how the same model could be extended to include other anomaly detection mechanisms.","PeriodicalId":273743,"journal":{"name":"IFIP/IEEE Eighth International Symposium on Integrated Network Management, 2003.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"A scaled, immunological approach to anomaly countermeasures: combining pH with cfengine\",\"authors\":\"Kyrre M. Begnum, M. Burgess\",\"doi\":\"10.1109/INM.2003.1194158\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We discuss the combination of two anomaly detection models, the Linux kernel module pH and cfengine, in order to create a multi-scaled approach to computer anomaly detection with automated response. By examining the time-average data from pH, we find the two systems to be conceptually complementary and to have compatible data models. Based on these findings, we build a simple prototype system and comment on how the same model could be extended to include other anomaly detection mechanisms.\",\"PeriodicalId\":273743,\"journal\":{\"name\":\"IFIP/IEEE Eighth International Symposium on Integrated Network Management, 2003.\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IFIP/IEEE Eighth International Symposium on Integrated Network Management, 2003.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INM.2003.1194158\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IFIP/IEEE Eighth International Symposium on Integrated Network Management, 2003.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INM.2003.1194158","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A scaled, immunological approach to anomaly countermeasures: combining pH with cfengine
We discuss the combination of two anomaly detection models, the Linux kernel module pH and cfengine, in order to create a multi-scaled approach to computer anomaly detection with automated response. By examining the time-average data from pH, we find the two systems to be conceptually complementary and to have compatible data models. Based on these findings, we build a simple prototype system and comment on how the same model could be extended to include other anomaly detection mechanisms.