DoSTDM: A denial of service detection model using firewall data traffic pattern matching

Abstract

This research deals with Denial of Service (DoS) flooding attacks. These types of attacks toward internet connected networks are on the rise. The research proposes a model that triangulate between statistical and neural network forecasting approaches. The proposed model can identify DoS attacks using the firewall rejected traffic falling outside the normal levels that could indicate a DoS attack. The triangulation approach provided the research model with multi prediction techniques with high accuracy.