Distributed denial of service attack (DDoS) is a common threat in today’s networks. While such an attack is not difficult to launch, defending a network resource against it is disproportionately difficult. This paper analysis some famous theories and methods on detection of DDoS network attacks systematically based on the fast neural network algorithm. Meanwhile, the attack traffic feature model which is constructed based on the packet length, packet transmission time interval and packet length change rate etc is proposed. Second, a method to optimize the parameters of the neural network error is also proposed by a large number of attempts. Finally, the UCLA dataset is used to carry out the contrast experiment of the parameters before and after the improvement. Experiments show that the proposed method can effectively detect DDoS attacks and has a better generalization ability.
