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Web-Based Intelligence for IDS

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11637))

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Abstract

We and others have shown that machine learning can detect and mitigate web-based attacks and the propagation of malware. High performance machine learning frameworks exist for the major computer languages used to program both web servers and web pages. This paper examines the factors required to use the frameworks as an effective distributed deterrent.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, USA

    Christopher B. Freas & Robert W. Harrison

Authors
  1. Christopher B. Freas
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  2. Robert W. Harrison
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Corresponding author

Correspondence to Robert W. Harrison .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Huazhong University of Science and Technology, Wuhan, China

    Jun Feng

  3. Fordham University, New York City, NY, USA

    Md Zakirul Alam Bhuiyan

  4. University of New Brunswick, Fredericton, NB, Canada

    Rongxing Lu

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Freas, C.B., Harrison, R.W. (2019). Web-Based Intelligence for IDS. In: Wang, G., Feng, J., Bhuiyan, M., Lu, R. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2019. Lecture Notes in Computer Science(), vol 11637. Springer, Cham. https://doi.org/10.1007/978-3-030-24900-7_25

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  • DOI: https://doi.org/10.1007/978-3-030-24900-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24899-4

  • Online ISBN: 978-3-030-24900-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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