Publication detail

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

ČEŠKA, M. HAVLENA, V. HOLÍK, L. LENGÁL, O. VOJNAR, T.

Original Title

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

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

reduction, nondeterministic finite automata, deep packet inspection, high-speed network monitoring 

Authors

ČEŠKA, M.; HAVLENA, V.; HOLÍK, L.; LENGÁL, O.; VOJNAR, T.

Released

1. 10. 2020

ISBN

1433-2779

Periodical

International Journal on Software Tools for Technology Transfer

Year of study

22

Number

5

State

Federal Republic of Germany

Pages from

523

Pages to

539

Pages count

17

URL

https://link.springer.com/article/10.1007/s10009-019-00520-8

BibTex

@article{BUT161576,
  author="Milan {Češka} and Vojtěch {Havlena} and Lukáš {Holík} and Ondřej {Lengál} and Tomáš {Vojnar}",
  title="Approximate Reduction of Finite Automata for High-Speed Network Intrusion Detection",
  journal="International Journal on Software Tools for Technology Transfer",
  year="2020",
  volume="22",
  number="5",
  pages="523--539",
  doi="10.1007/s10009-019-00520-8",
  issn="1433-2779",
  url="https://link.springer.com/article/10.1007/s10009-019-00520-8"
}

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