Abnormal flow detection technology in GPU network based on statistical classification method

Abstract

Domain Name System (DNS), as the Internet “hub system” of basic resources services, mainly provides the basic services of domain name and IP address mapping. Abnormal flow detection technology plays an important role in the security service quality of Internet basic services, and it is also one of the important contents of Internet security research. The existing research mainly focuses on the analysis of network flow and other technologies at the data level, but in the context of network attacks, especially in the case of DDoS attacks, the accuracy and detection performance need to be improved. Based on the statistical method of high-performance abnormal flow detection technology, in this paper, the flow data are used for real-time statistical fitting, and the difference is made with the historical log data statistics. GPU parallel technology is used to improve the detection performance, which improves the accuracy and detection performance in the case of DDoS attacks on the network.