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NETWORK ABNORMAL STATE DETECTION DEVICE USING HMM(HIDDEN MARKOV MODEL) AND METHOD THEREOF

机译:HMM(隐马尔可夫模型)的网络异常状态检测装置及其方法

摘要

A device and a method for detecting a probabilistic abnormal state of network using Hidden Markov Model are provided to enhance the accuracy of determining a network error by HMM(Hidden Markov Model). An error symptom extracting unit(410) analyzes a network traffic and outputs the network traffic characteristic. An error symptom determining unit(420) uses a network traffic characteristic as an input value, and outputs each state of network by a probability value. The error symptom extracting unit comprises a traffic input unit(412), a packet processing unit(414), a log recording unit(416), and a network traffic characteristic outputting unit(418). The packet processing unit processes the inputted network traffic as packet-by-packet to check the status of the network traffic.
机译:提供一种使用隐马尔可夫模型来检测网络的概率异常状态的装置和方法,以提高通过HMM(隐马尔可夫模型)确定网络错误的准确性。错误症状提取单元(410)分析网络流量并输出网络流量特性。错误症状确定单元(420)使用网络流量特性作为输入值,并以概率值输出网络的每种状态。错误症状提取单元包括业务量输入单元(412),分组处理单元(414),日志记录单元(416)和网络业务量特征输出单元(418)。分组处理单元逐个分组地处理输入的网络流量,以检查网络流量的状态。

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