MF研究者総覧

教員活動データベース

An NMF-Based Traffic Classification Approach towards Anomaly Detection for Massive Sensors

発表形態:
原著論文
主要業績:
主要業績
単著・共著:
共著
発表年月:
2014年09月
DOI:
10.1109/INCoS.2014.92
会議属性:
国際会議(国内開催を含む)
査読:
有り
リンク情報:

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 Hori
Title:
An NMF-Based Traffic Classification Approach towards Anomaly Detection for Massive Sensors
Announcement information:
Proceedings of 2014 International Conference on Intelligent Networking and Collaborative Systems (INCoS 2014) Page: 396-399
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.
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.


Copyright © MEDIA FUSION Co.,Ltd. All rights reserved.