Behavioral Anomaly Detection System on Network Application Traffic from Many Sensors

Akira Nagata, Kohei Kotera, Katsuichi Nakamura, Y. Hori
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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).
多传感器网络应用流量行为异常检测系统
针对大数据时代的计算机网络,讨论了一种行为异常检测系统,使异常流量行为分析和即时检测成为可能。许多传感器设备连接到网络,并倾向于以相当低的通信速率生成其应用程序流量。为了在短时间内观察到流量分析所需的流量信息,监控系统将被认为产生同一应用的设备发出的流量统计信息进行整合。该方法采用非负矩阵分解(Non-Negative Matrix Factorization, NMF)方法,在应用分析的基础上检测异常流量行为。
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