DDoS defense system with turing test and neural network

Abstract

Distributed Denial of Service (DDoS) attack presents the following characteristics, that the botnets become extra-large scale, the mode of attack presents a variety of characteristics and the application-level attacks become the main attack approach, which seriously impact on Internet Security. However, traditional software defense detection means have such problem, that the accurate rate is too low, detecting method is excessively obsolete and detecting way is excessively passive and the deployment of defense system is cumbersome. While hardware defense system such as ACL and IDMS products costs much, which small or medium-sized website has no ability to bear it. For the above reasons, we try to use artificial intelligence methods. Using the Turing test method to detect users, who do the behavior. Using modified RBF neural network to detect attack, designing intelligent user control system to deal with the complex and ever-changing attacks. The test results show that this defense system cost lowly, own strong defense capability, has the ability to deal with the current distributed denial of service attacks and impact on the server running performance less.