Anomaly Detection of Internet Traffic using Robust Feature Selection based on Kernel Density Estimation

Anomaly detection of Internet traffic is a network service of primary importance, given the constant threats that impinge on Internet security. From a statistical perspective, traffic anomalies can be considered outliers, and must be handled through effective outlier detection methods, for which feature selection is an important pre-processing step. Feature selection removes the redundant and irrelevant features from the detection process, increasing its performance. In this work, we consider outlier detection based on principal component analysis, and feature selection based on mutual information. Moreover, we address the use of kernel density estimation (KDE) to estimate the mutual information, which is designed for continuous features, and avoids the discretization step of histograms. Our results, obtained using a high-quality ground-truth, clearly show the usefulness of feature selection and the superiority of KDE to estimate the mutual information, in the context of Internet traffic anomaly detection.