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 larg
FPGA targeted implementation of a neurofuzzy system for real time TCP/IP traffic classification / Cinti, Alessandro; Rizzi, Antonello. - (2013), pp. 312-317. (Intervento presentato al convegno 2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 tenutosi a Hangzhou; China nel 19 October 2013 through 21 October 2013) [10.1109/icaci.2013.6748522].
FPGA targeted implementation of a neurofuzzy system for real time TCP/IP traffic classification
CINTI, ALESSANDRO;RIZZI, Antonello
2013
Abstract
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 largI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.