Parallel botnet detection system by using GPU

In recent years, botnet is one of the major threats to network security. Many approaches have been proposed to detect botnets by comparing bot features. Usually, these approaches adopt traffic reduction strategy as first step to reduce the flow to following strategies by filtering packets. With the rapid development of network hardware and software the network speed has reached to multi-gigabit. However, analyzing header and payload of every packet consumes huge amount of computational resources and is not suitable for many realistic situations. Although signature-based solutions are accurate, it is not possible to detect bot variants in real-time. In this study, we proposed a GPU-based botnet detection approach. The experimental results show that the network traffic reduction stage on GPU can achieve about 8x times over CPU based botnet detection tool. The proposed algorithm can used to improve the performance of botnet detection tools efficiently.