Detecting Denial of Service Attacks with Bayesian Classifiers and the Random Neural Network
Denial of Service (DoS) is a prevalent threat in today's networks. While such an attack is not difficult to launch, defending a network resource against it is disproportionately difficult, and despite the extensive research in recent years, DoS attacks continue to harm. The first goal of any protection scheme against DoS is the detection of its existence, ideally long before the destructive traffic build-up. In this paper the authors propose a generic approach which uses multiple bayesian classifiers, and they present and compare four different implementations of it, combining likelihood estimation and the Random Neural Network (RNN).