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Network Anomaly Detection Based on Statistical Approach and Time Series Analysis

Author: HuangZuo
Tutor: LiangALei
School: Shanghai Jiaotong University
Course: Software Engineering
Keywords: Network Anomaly Detection Expectation Maximization algorithm Gaussian mixture model K D indicator approach Smooth Moving Average Convergence Divergence Timing Analysis
CLC: TP393.06
Type: Master's thesis
Year: 2009
Downloads: 103
Quote: 0
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Abstract


Network frequently router transmission rate changes , the device restart worm attack , abnormal fluctuations . Network anomalies as soon as possible to avoid the occurrence of serious problems in the future and the rapid recovery of the disaster to play a crucial role . This has provided a guarantee to provide a stable network transmission . In this paper , we use statistical methods to analyze the distribution of network traffic to identify network traffic under normal circumstances . However , the traffic of different protocols have different statistical characteristics . Not a single distribution to match the distribution of network traffic . A lot of research work has been the basis for this conclusion . We experiment through the analysis of the actual data to verify this assertion . We adopt the expectation maximization algorithm to estimate the distribution of the Gaussian mixture model parameters widely used in the field of artificial intelligence . Once the statistical feature abnormal fluctuations or sudden changes of the network traffic , and exceeds a certain threshold alarm will be triggered. We use the time series analysis methods to deal with the results of statistical analysis of data . In the first method , determined by analysis of the upper and lower bounds . If the parameter is out of the upper and lower bounds on the judgment for an exception occurs . Another time series analysis method called by two K D index line staggered to reflect abnormal fluctuations in the network . Portrayed two indicator lines are similar to the mean of the historical data . The one for which the latest data volatility is more sensitive , while the other one is more dull . The third method is the MACD indicator line . Such methods and K, D method is very similar to , but is relatively less sensitive to volatility . Through experiments we found that abnormal judgment but more correct . Our experiment eventually proved the effectiveness of the method .

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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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