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Detection of Web Application Attacks with Request Length Module and Regex Pattern Analysis

  • Conference paper
  • First Online:
Genetic and Evolutionary Computing (GEC 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 388))

Included in the following conference series:

  • International Conference on Genetic and Evolutionary Computing

Abstract

Web application attack detection is one of the popular research areas during these years. Security for web application is necessary and it will be effective to study and analyze how malicious patterns occur in web server log. This system analyzes web server log file, which includes normal and malicious users’ access patterns with their relevant links. This uses web server log file dataset for the detection of web application attacks. This system intends to analyze normal and attack behaviors from web server log and then classify attack types which are included in the dataset. In this system, three types of attacks are detected namely, SQL injection, XSS and directory traversal attacks. Attack analysis stage is done by request length module and regular expressions for various attack patterns.

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References

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Author information

Authors and Affiliations

  1. University of Computer Studies, Yangon, Myanmar

    Ei Ei Han

Authors
  1. Ei Ei Han
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Corresponding author

Correspondence to Ei Ei Han .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. School of Computer Science and Tech..., Harbin Institute of Technology, Shenzhen Graduate School , Shenzhen, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engg, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  4. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Pyke Tin

  5. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Mitsuhiro Yokota

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© 2016 Springer International Publishing Switzerland

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Han, E.E. (2016). Detection of Web Application Attacks with Request Length Module and Regex Pattern Analysis. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. GEC 2015. Advances in Intelligent Systems and Computing, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-319-23207-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-23207-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23206-5

  • Online ISBN: 978-3-319-23207-2

  • eBook Packages: EngineeringEngineering (R0)

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