Semi-unsupervised machine learning for anomaly detection in HTTP traffic

Abstract

Currently, the growing popularity of publicly available web services is one of the driving forces for so-called “web hacking” activities. The main contribution of this paper is the semi-unsupervised anomaly detection method for HTTP traffic anomaly detection. We made the assumption that during the learning phase (for the captured volume of HTTP traffic), only small friction of samples is labelled. Our experiments show that the proposed method allows us to achieve the ratios of true positive and false positive errors below 1%.