摘要: In our previous work, we have applied ordinary linear regression equation to network anomaly detection. However, the performance of is susceptible outliers. Unfortunately, it almost impossible obtain a “clean” traffic data set for model due burstiness and pervasive attacks. this paper, make use robust techniques mitigate impact outliers in training set. The experiment results show that based method more reliable than face
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