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Prevention and Fighting against Web Attacks through Anomaly Detection Technology. A Systematic Review

Author

Listed:
  • Tomás Sureda Riera

    (Computer Science Department, University of Alcalá, Alcalá de Henares, 28805 Madrid, Spain)

  • Juan-Ramón Bermejo Higuera

    (Escuela Superior de Ingeniería y Tecnología (ESIT), Universidad Internacional de la Rioja (UNIR), Logroño, 26006 La Rioja, Spain)

  • Javier Bermejo Higuera

    (Escuela Superior de Ingeniería y Tecnología (ESIT), Universidad Internacional de la Rioja (UNIR), Logroño, 26006 La Rioja, Spain)

  • José-Javier Martínez Herraiz

    (Computer Science Department, University of Alcalá, Alcalá de Henares, 28805 Madrid, Spain)

  • Juan-Antonio Sicilia Montalvo

    (Escuela Superior de Ingeniería y Tecnología (ESIT), Universidad Internacional de la Rioja (UNIR), Logroño, 26006 La Rioja, Spain)

Abstract

Numerous techniques have been developed in order to prevent attacks on web servers. Anomaly detection techniques are based on models of normal user and application behavior, interpreting deviations from the established pattern as indications of malicious activity. In this work, a systematic review of the use of anomaly detection techniques in the prevention and detection of web attacks is undertaken; in particular, we used the standardized method of a systematic review of literature in the field of computer science, proposed by Kitchenham. This method is applied to a set of 88 papers extracted from a total of 8041 reviewed papers, which have been published in notable journals. This paper discusses the process carried out in this systematic review, as well as the results and findings obtained to identify the current state of the art of web anomaly detection.

Suggested Citation

  • Tomás Sureda Riera & Juan-Ramón Bermejo Higuera & Javier Bermejo Higuera & José-Javier Martínez Herraiz & Juan-Antonio Sicilia Montalvo, 2020. "Prevention and Fighting against Web Attacks through Anomaly Detection Technology. A Systematic Review," Sustainability, MDPI, vol. 12(12), pages 1-45, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:12:p:4945-:d:372713
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    References listed on IDEAS

    as
    1. Gilberto Fernandes & Joel J. P. C. Rodrigues & Luiz Fernando Carvalho & Jalal F. Al-Muhtadi & Mario Lemes Proença, 2019. "A comprehensive survey on network anomaly detection," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 70(3), pages 447-489, March.
    2. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
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    Cited by:

    1. Jonghyeon Ko & Marco Comuzzi, 2023. "A Systematic Review of Anomaly Detection for Business Process Event Logs," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(4), pages 441-462, August.

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