日本語フィールド
著者：Akira Nagata, Kohei Kotera, Katsuichi Nakamura, Yoshiaki Hori題名：An NMF-Based Traffic Classification Approach towards Anomaly Detection for Massive Sensors発表情報：Proceedings of 2014 International Conference on Intelligent Networking and Collaborative Systems (INCoS 2014) ページ: 396-399キーワード：概要：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 traffic for the same application. It detects anomaly traffic behavior on the basis of application analysis using NMF(Non-Negative Matrix Factorization). This paper describes a basic design of our prototype development.抄録：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 traffic for the same application. It detects anomaly traffic behavior on the basis of application analysis using NMF(Non-Negative Matrix Factorization). This paper describes a basic design of our prototype development.英語フィールド
Author：Akira Nagata, Kohei Kotera, Katsuichi Nakamura, Yoshiaki HoriTitle：An NMF-Based Traffic Classification Approach towards Anomaly Detection for Massive SensorsAnnouncement information:Proceedings of 2014 International Conference on Intelligent Networking and Collaborative Systems (INCoS 2014) Page: 396-399An 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 traffic for the same application. It detects anomaly traffic behavior on the basis of application analysis using NMF(Non-Negative Matrix Factorization). This paper describes a basic design of our prototype development.An 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 traffic for the same application. It detects anomaly traffic behavior on the basis of application analysis using NMF(Non-Negative Matrix Factorization). This paper describes a basic design of our prototype development.