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Research on Network Traffic Anomaly Detection Method Using Degree Distributions

Author: WangWuZuo
Tutor: WuWeiDong
School: Wuhan University of Science and Technology
Course: Applied Computer Technology
Keywords: Anomaly Detection Flow profile Degree distribution Information entropy
CLC: TP393.06
Type: Master's thesis
Year: 2011
Downloads: 26
Quote: 1
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Abstract


With the growing scale of the network , the network would often deviate from normal network behavior anomaly flow . These abnormal flow not only cause the decline in network performance , cause more serious down the network . Therefore , how large - scale network environment , network traffic anomaly detection , of great significance to protect the normal operation of the network . Network traffic abnormality detection is the use of the normal traffic behavior model has been established to detect the degree of deviation of the current flow behavior to determine whether a traffic abnormality . Traffic anomaly detection face three key challenges : the characteristic quantities choice , the determination of the threshold comparison frequency selected . To solve the above challenges , this paper presents a degree distribution of network traffic anomaly detection method . Firstly, the flow patterns of behavior based on network flow profile , and then introduced to describe the degree distribution of the flow profile situation , the change of the the mapping flow profile of the distribution by calculating the degree of information entropy , Finally Chebyshev inequality set flow normal range upper and lower limits , comparing each time window degree distribution entropy changes , according to the entropy degree of deviation of the normal region , the flow rate the degree of abnormality of the classification to achieve a graded alarm mechanism , in order to detect the flow exception. The use of real and simulated network traffic data , and conducted a series of experiments to demonstrate the availability and effectiveness of the detection method proposed in this paper . Especially better detection effect on short-time network traffic anomaly detection capability with real network environment .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > Computer networks, test , run
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