Akira Nagata, Kohei Kotera, Katsuichi Nakamura, Y. Hori
{"title":"多传感器网络应用流量行为异常检测系统","authors":"Akira Nagata, Kohei Kotera, Katsuichi Nakamura, Y. Hori","doi":"10.1109/COMPSAC.2014.85","DOIUrl":null,"url":null,"abstract":"For a computer network in the era of big data, we discuss a behavioral anomaly detection system which makes it possible to analyze and immediately detect anomaly traffic behavior. Many sensor devices connect to the network and tend to generate their application traffic at quite a low communication rate. In order to observe necessary traffic information for traffic analysis in a short time, the monitoring system integrates traffic statistics of flows sent from devices which are considered to generate the same application. It detects anomaly traffic behavior on the basis of application analysis using NMF(Non-Negative Matrix Factorization).","PeriodicalId":106871,"journal":{"name":"2014 IEEE 38th Annual Computer Software and Applications Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Behavioral Anomaly Detection System on Network Application Traffic from Many Sensors\",\"authors\":\"Akira Nagata, Kohei Kotera, Katsuichi Nakamura, Y. Hori\",\"doi\":\"10.1109/COMPSAC.2014.85\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For a computer network in the era of big data, we discuss a behavioral anomaly detection system which makes it possible to analyze and immediately detect anomaly traffic behavior. Many sensor devices connect to the network and tend to generate their application traffic at quite a low communication rate. In order to observe necessary traffic information for traffic analysis in a short time, the monitoring system integrates traffic statistics of flows sent from devices which are considered to generate the same application. It detects anomaly traffic behavior on the basis of application analysis using NMF(Non-Negative Matrix Factorization).\",\"PeriodicalId\":106871,\"journal\":{\"name\":\"2014 IEEE 38th Annual Computer Software and Applications Conference\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 38th Annual Computer Software and Applications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMPSAC.2014.85\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 38th Annual Computer Software and Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPSAC.2014.85","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Behavioral Anomaly Detection System on Network Application Traffic from Many Sensors
For a computer network in the era of big data, we discuss a behavioral anomaly detection system which makes it possible to analyze and immediately detect anomaly traffic behavior. Many sensor devices connect to the network and tend to generate their application traffic at quite a low communication rate. In order to observe necessary traffic information for traffic analysis in a short time, the monitoring system integrates traffic statistics of flows sent from devices which are considered to generate the same application. It detects anomaly traffic behavior on the basis of application analysis using NMF(Non-Negative Matrix Factorization).