Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Enhancing byte-level network intrusion detection signatures with context
Proceedings of the 10th ACM conference on Computer and communications security
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
Advanced algorithms for fast and scalable deep packet inspection
Proceedings of the 2006 ACM/IEEE symposium on Architecture for networking and communications systems
An improved algorithm to accelerate regular expression evaluation
Proceedings of the 3rd ACM/IEEE Symposium on Architecture for networking and communications systems
Curing regular expressions matching algorithms from insomnia, amnesia, and acalculia
Proceedings of the 3rd ACM/IEEE Symposium on Architecture for networking and communications systems
A hybrid finite automaton for practical deep packet inspection
CoNEXT '07 Proceedings of the 2007 ACM CoNEXT conference
Deflating the big bang: fast and scalable deep packet inspection with extended finite automata
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
An improved DFA for fast regular expression matching
ACM SIGCOMM Computer Communication Review
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Deep Packet Inspection is required in an increasing number of network devices, in order to improve network security and provide application-specific services. Instead of standard strings to represent the data set to be matched, state-of-the-art systems adopt regular expressions, due to their high expressive power and flexibility. Typically regular expressions are matched through deterministic finite automata (DFAs), but large rule sets need a memory amount which turns out to be too large for practical implementation. Many recent works have proposed improvements to address this issue, but they increase the number of transitions (and then of memory accesses) per character. In a previous work, we have presented a smart representation for DFA which, while preserving fast matching (i.e., a transition per character only), considerably reduces states and transitions. In this paper we introduce a novel optimized automaton, which exploits second order relationships within the DFA and is based on the key concept of "temporary transitions". Results for real data sets show that it allows for a further memory saving.