The Research of Application Layer DDoS Attack Detection based the Model of Human Access

With the application and popularization of the computer network, people have increasingly intense for network security requires. In the field of network security, DDoS attacks are considered one of the most destructive of the network attack, the destructive is more powerful with increasing of the network bandwidth. For the traditional network layer DDoS attack detection and prevention tools are maturing, the attacker then to a higher level- application-layer DDoS attacks. In this paper, a method for unsupervised learning to detect application-layer DDoS attacks, and specific network scenarios for digital simulation, through the analysis of simulation results, determine the areas of the network being attacked. Through the analysis of different types of visitors in the distribution networks of neurons, highlighting the difference between the attacker access and the user's access behavior. Through these provides a theoretical for the DDoS application layer detection and prevention in the future.