Denial of service detection by support vector machines and radial-basis function neural network

Denial of service (DoS) problem is one of serious attacks in the Internet. The attackers attempt to exhaust the resource of the service provider in order to prevent legitimate users from using the system. Most of the detecting DoS tools, such as rule-based and threshold detection approaches, rely on the objective opinion of the domain experts. This work aims to apply machine learning techniques, such as radial-basis function neural network (RBFNN) and support vector machines (SVM), to solve the DoS problem and compare which technique, is better to detect DoS. The main advantage of this detection method is that it has the ability to detect or predict new attacks when some patterns are similar to the attack patterns learnt in the past. Thus it can detect novel attacks for which signatures have not been defined.