Approximate Reduction of Finite Automata for High-Speed Network Intrusion Detection

Abstract
We consider the problem of approximate reduction of non-deterministic automata that appear in hardware-accelerated network intrusion detection systems (NIDSes). We define an error distance of a reduced automaton from the original one as the probability of packets being incorrectly classified by the reduced automaton (wrt the probabilistic distribution of packets in the network traffic). We use this notion to design an approximate reduction procedure that achieves a great size reduction (much beyond the state-of-the-art language preserving techniques) with a controlled and small error. We have implemented our approach and evaluated it on use cases from Snort , a popular NIDS. Our results provide experimental evidence that the method can be highly efficient in practice, allowing NIDSes to follow the rapid growth in the speed of networks.
Článek se zaobírá přibližnou redukcí konečných automatů pro detekci útoků ve vysokorychlostních sítích.
Description
Citation
Lecture Notes in Computer Science. 2018, vol. 10806, issue 2, p. 155-175.
https://www.fit.vut.cz/research/publication/11657/
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
Creative Commons Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
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