{"title":"基于LMS算法的在线异常检测方法","authors":"Ziyu Wang, Jiahai Yang, Fuliang Li","doi":"10.1109/APNOMS.2014.6996537","DOIUrl":null,"url":null,"abstract":"Anomaly detection has been a hot topic in recent years due to its capability of detecting zero attacks. In this paper, we propose a new on-line anomaly detection method based on LMS algorithm. The basic idea of the LMS-based detector is to predict IGTE using IGFE, given the high linear correlation between them. Using the artificial synthetic data, it is shown that the LMS-based detector possesses strong detection capability, and its false positive rate is within acceptable scope.","PeriodicalId":269952,"journal":{"name":"The 16th Asia-Pacific Network Operations and Management Symposium","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"An on-line anomaly detection method based on LMS algorithm\",\"authors\":\"Ziyu Wang, Jiahai Yang, Fuliang Li\",\"doi\":\"10.1109/APNOMS.2014.6996537\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Anomaly detection has been a hot topic in recent years due to its capability of detecting zero attacks. In this paper, we propose a new on-line anomaly detection method based on LMS algorithm. The basic idea of the LMS-based detector is to predict IGTE using IGFE, given the high linear correlation between them. Using the artificial synthetic data, it is shown that the LMS-based detector possesses strong detection capability, and its false positive rate is within acceptable scope.\",\"PeriodicalId\":269952,\"journal\":{\"name\":\"The 16th Asia-Pacific Network Operations and Management Symposium\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 16th Asia-Pacific Network Operations and Management Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APNOMS.2014.6996537\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 16th Asia-Pacific Network Operations and Management Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APNOMS.2014.6996537","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An on-line anomaly detection method based on LMS algorithm
Anomaly detection has been a hot topic in recent years due to its capability of detecting zero attacks. In this paper, we propose a new on-line anomaly detection method based on LMS algorithm. The basic idea of the LMS-based detector is to predict IGTE using IGFE, given the high linear correlation between them. Using the artificial synthetic data, it is shown that the LMS-based detector possesses strong detection capability, and its false positive rate is within acceptable scope.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
