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Network Anomaly Detection and Analysis Base on Sliding Window Wavelet Binary Tree

Author: ZhuXiaoFeng
Tutor: WangKaiDong
School: Xi'an University of Electronic Science and Technology
Course: Computer System Architecture
Keywords: Wavelet binary sliding window ARX ??model Clustering EM algorithm Anomaly Detection Real-time algorithm
CLC: TP393.08
Type: Master's thesis
Year: 2010
Downloads: 34
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With the rapid development and popularization of the Internet , the demand for network resources is also increasing rapidly ; while the network also contains a large number of network attacks , and its impact on network performance is growing. Network attacks are a direct manifestation of network traffic anomalies , which calls for fast and efficient detection of an abnormality . This paper presents a new network anomaly detection and analysis methods, through the sliding window wavelet binary and ARX model for network traffic modeling and prediction, and then by clustering the EM algorithm to categorize and detect anomalies . This paper describes the characteristics of the sliding window binary wavelet and calculation methods, such as it can solve the sliding window is generated when the data redundancy ; and the original window to update the data , but also be able to update the corresponding decomposition level wavelet coefficients , reflecting the coefficient update in real time sex . ARX ??model followed by system identification modeling of wavelet coefficients for each layer to obtain the model residuals , and to explore its adaptive time series forecasting applications . Then explore the Gaussian mixture model , EM clustering algorithm theory , and ARX model residuals for cluster analysis and outlier detection . Through the analysis of the mathematical model of collaboration , the algorithm for network traffic anomaly detection, and better reflects the real-time nature . Final data set using KDDCup99 do network traffic anomaly detection experimental results confirm the anomaly detection method can obtain higher rates .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > Computer Network Security
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