Detection and Prevention of Denial of Service Attacks Using Distributed Denial-of-Service Detection Mechanism

In the networking systems the flow of information is the most important service. It is clear that a simple self-propagating worm can quickly spread across the Internet and cause severe damage to our society. Facing this great security threats like Denial-of-Service (DoS), we need to build an early detection system that can detect the presence of a worm in the Internet as quickly as possible in order to give people accurate early warning information and possible reaction time for counteractions. To avoid these types of threats more effective approaches are required to counter the threats. This requirement has motivated us to create novel mechanism for effective early detection and prevention of DoS attacks at the router level within an Internetworking infrastructure. Here our system presents a “trend detection” methodology to detect a DoS at its early propagation stage by using Kalman filter. In addition, for uniform-scan worms such as Code Red, we can effectively predict the overall vulnerable population size, and estimate accurately how many computers are really infected in the global Internet based on the biased monitored data. Also in this system we propose a domain-based approach, the mechanism that combines both stateful and stateless signatures to provide early detection of DoS attacks, therefore, protect the network. In this project we are using the novel Distributed DoS Detection Mechanism (DiDDeM) using the Kalman filter mechanism to detect DoS attacks at the early stage.