FPGA targeted implementation of a neurofuzzy system for real time TCP/IP traffic classification

As Internet traffic grows rapidly, it is necessary to monitor and control TCP/IP flows in order to ensure the quality of service and to filter out unwanted traffic by automatic, effective and inexpensive technical solutions. To this aim, especially when dealing with Gbit/s links, real time TCP/IP traffic classification can be performed by dedicated high speed processing devices, avoiding computationally expensive deep packet inspection techniques and relying only on packet features independent of payload content. In this paper we propose to employ an FPGA to design a stand-alone device using only information available at network layer, namely packet sizes, directions and inter-arrival times, to perform flow classification according to application layer protocol (such as HTTP, FTP, SSH, POP3, etc.). The classification system is based on neurofuzzy Min-Max networks, trained by Adaptive Resolution procedures (ARC and PARC algorithms). In order to deal with very high speed links and a large amount of concurrent traffic flows, we propose a complete FPGA targeted implementation of the whole system. Our design is intended to place on a single FPGA all the needed components, including the neurofuzzy Min-Max classifier. The paper describes in detail some interesting technical solutions aiming at optimizing both FPGA working frequency and circuit complexity.