A similarity based approach for application DoS attacks detection

The ability to identify anomalous traffic patterns is a central issue for network managers: primarily lots of problems could arise from network attacks, such as viruses and tunneling tools. In this paper we present a detection algorithm able to extract information analyzing features of the network traffic con- taining attacks. The algorithm exploits statistical methodologies for traffic categorization. To assess the practical usability of the proposed algorithms we have tested its application in a case of abuse of resources through an application DoS attack known as slowloris. We have obtained an excellent reliability both analyzing single samples of traffic (100% of anomalies detection, with 1% probability of false positives) and processing multiple samples, through an average measurement (100% of anomalies detection, with a distance between traffics of 5.29 sigma, providing an extremely low false positive error rate).