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 proceedingConference contributionAcademicpeer-review

    1 Citation (Scopus)
    11 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 languageUndefined
    Title of host publicationFirst International Conference on Autonomous Infrastructure, Management and Security
    EditorsArosha K. Bandara, Mark Burgess
    Place of PublicationHeidelberg
    PublisherSpringer
    Pages196-199
    Number of pages4
    ISBN (Print)978-3-540-72985-3
    DOIs
    Publication statusPublished - Jun 2007

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    NumberLNCS4549
    Volume4543

    Keywords

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

    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). Heidelberg: Springer. https://doi.org/10.1007/978-3-540-72986-0_24, https://doi.org/10.1007/978-3-540-72986-0