A Survey of the High-Speed Self-Learning Intrusion Detection Research Area

Anna Sperotto; R. van de Meent

doi:10.1007/978-3-540-72986-0_24

A Survey of the High-Speed Self-Learning Intrusion Detection Research Area

Anna Sperotto, R. van de Meent

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

18 Downloads (Pure)

Abstract

Intrusion detection for IP networks has been a research theme for a number of years already. One of the challenges is to keep up with the ever increasing Internet usage and network link speeds, as more and more data has to be scanned for intrusions. Another challenge is that it is hardly feasible to adapt the scanning configuration to new threats manually in a timely fashion, because of the possible rapid spread of new threats. This paper is the result of the first three months of a PhD research project in high speed, self-learning network intrusion detection systems. Here, we give an overview of the state of the art in this field, highlighting at the same time the major open issues.

Original language	Undefined
Title of host publication	First International Conference on Autonomous Infrastructure, Management and Security
Editors	Arosha K. Bandara, Mark Burgess
Place of Publication	Heidelberg
Publisher	Springer
Pages	196-199
Number of pages	4
ISBN (Print)	978-3-540-72985-3
DOIs	https://doi.org/10.1007/978-3-540-72986-0_24 https://doi.org/10.1007/978-3-540-72986-0
Publication status	Published - Jun 2007
Event	First International Conference on Autonomous Infrastructure, Management and Security, Oslo, Norway: First International Conference on Autonomous Infrastructure, Management and Security - Heidelberg Duration: 1 Jun 2007 → …

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Number	LNCS4549
Volume	4543

Conference

Conference	First International Conference on Autonomous Infrastructure, Management and Security, Oslo, Norway
City	Heidelberg
Period	1/06/07 → …

Keywords

Intrusion Detection
High speed Networks
IR-61871
METIS-241813
EWI-10822

Access to Document

aims2007_formatted

Cite this

Sperotto, A., & van de Meent, R. (2007). A Survey of the High-Speed Self-Learning Intrusion Detection Research Area. In A. K. Bandara, & M. Burgess (Eds.), First International Conference on Autonomous Infrastructure, Management and Security (pp. 196-199). (Lecture Notes in Computer Science; Vol. 4543, No. LNCS4549). Springer. https://doi.org/10.1007/978-3-540-72986-0_24, https://doi.org/10.1007/978-3-540-72986-0

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abstract = "Intrusion detection for IP networks has been a research theme for a number of years already. One of the challenges is to keep up with the ever increasing Internet usage and network link speeds, as more and more data has to be scanned for intrusions. Another challenge is that it is hardly feasible to adapt the scanning configuration to new threats manually in a timely fashion, because of the possible rapid spread of new threats. This paper is the result of the first three months of a PhD research project in high speed, self-learning network intrusion detection systems. Here, we give an overview of the state of the art in this field, highlighting at the same time the major open issues.",

keywords = "Intrusion Detection, High speed Networks, IR-61871, METIS-241813, EWI-10822",

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Sperotto, A & van de Meent, R 2007, A Survey of the High-Speed Self-Learning Intrusion Detection Research Area. in AK Bandara & M Burgess (eds), First International Conference on Autonomous Infrastructure, Management and Security. Lecture Notes in Computer Science, no. LNCS4549, vol. 4543, Springer, Heidelberg, pp. 196-199, First International Conference on Autonomous Infrastructure, Management and Security, Oslo, Norway, Heidelberg, 1/06/07. https://doi.org/10.1007/978-3-540-72986-0_24, https://doi.org/10.1007/978-3-540-72986-0

A Survey of the High-Speed Self-Learning Intrusion Detection Research Area. / Sperotto, Anna; van de Meent, R.

First International Conference on Autonomous Infrastructure, Management and Security. ed. / Arosha K. Bandara; Mark Burgess. Heidelberg : Springer, 2007. p. 196-199 (Lecture Notes in Computer Science; Vol. 4543, No. LNCS4549).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Sperotto A, van de Meent R. A Survey of the High-Speed Self-Learning Intrusion Detection Research Area. In Bandara AK, Burgess M, editors, First International Conference on Autonomous Infrastructure, Management and Security. Heidelberg: Springer. 2007. p. 196-199. (Lecture Notes in Computer Science; LNCS4549). doi: 10.1007/978-3-540-72986-0_24, 10.1007/978-3-540-72986-0