Robust regression for anomaly detection

Abstract

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