Traffic classification on the fly
ACM SIGCOMM Computer Communication Review
Algorithms to accelerate multiple regular expressions matching for deep packet inspection
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Traffic classification through simple statistical fingerprinting
ACM SIGCOMM Computer Communication Review
Identifying and discriminating between web and peer-to-peer traffic in the network core
Proceedings of the 16th international conference on World Wide Web
Deflating the big bang: fast and scalable deep packet inspection with extended finite automata
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Efficient regular expression evaluation: theory to practice
Proceedings of the 4th ACM/IEEE Symposium on Architectures for Networking and Communications Systems
GT: picking up the truth from the ground for internet traffic
ACM SIGCOMM Computer Communication Review
An experimental evaluation of the computational cost of a DPI traffic classifier
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Toward the accurate identification of network applications
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Self-Learning IP traffic classification based on statistical flow characteristics
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Improving matching performance of DPI traffic classifier
Proceedings of the 2011 ACM Symposium on Applied Computing
Detection and classification of peer-to-peer traffic: A survey
ACM Computing Surveys (CSUR)
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Traffic classification through Deep Packet Inspection (DPI) is considered extremely expensive in terms of processing costs, leading to the conclusion that this technique is not suitable for DPI analysis on high speed networks. However, we believe that performance can be improved by exploiting some common characteristics of the network traffic. In this paper we present and evaluate some optimizations that can definitely decrease the processing cost and can even improve the classification precision.