Abstract
In recent years, with the rapid development of the network hardware and software, the network speed is enhanced to multi-gigabit. Network packet filtering is an important strategy of network security to avoid malicious attacks, and it is a computation-consuming application. Therefore, we develop two efficient GPGPU-based parallel packet classification approaches to filter packets by leveraging thousands of threads. The experiment results demonstrate that the computational efficiency of filtering packet can be significantly enhanced by using GPGPU.
Highlights
In the past few years, the development of network bandwidth and hardware technologies have grown rapidly, a variety of Internet services have been popular, such as email system, storage system, entertainment system and others
To improve the computational performance of the Botnet detection system, we propose two efficient network traffic reduction algorithms to filter packets simultaneously by using GPGPU device
We propose two fast packet filter methods by leveraging power of Graphics Processing Unit (GPU) device
Summary
In the past few years, the development of network bandwidth and hardware technologies have grown rapidly, a variety of Internet services have been popular, such as email system, storage system, entertainment system and others. Random packet losses are likely to occur if the network traffic exceeds the capacity of packet filter of the botnet detection system. To s olve packet filter problem leaded by large amounts of traffic, one of the solutions is to in crease the processing capacity of the botnet detection system. It can be either hardware[9] or software[10] solutions. To improve the computational performance of the Botnet detection system, we propose two efficient network traffic reduction algorithms to filter packets simultaneously by using GPGPU device. It p resents that GPGPU is useful for real-time traffic analysis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
