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Evolving Rules for Detecting Cross-Site Scripting Attacks Using Genetic Programming

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1347))

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Abstract

Web services are now a critical element of many of our day-to-day activities. Their applications are one of the fastest-growing industries around. The security issues related to these services are a major concern to their providers and are directly relevant to the everyday lives of system users. Cross-Site Scripting (XSS) is a standout amongst common web application security attacks. Protection against XSS injection attacks needs more work. Machine learning has considerable potential to provide protection in this critical domain. In this article, we show how genetic programming can be used to evolve detection rules for XSS attacks. We conducted our experiments on a publicly available and up-to-date dataset. The experimental results showed that the proposed method is an effective countermeasure against XSS attacks. We then investigated the computational cost of the detection rules. The best-evolved rule has a processing time of 177.87 ms and consumes memory of 8,600 bytes.

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Authors and Affiliations

  1. Department of Computer Science, University of Kufa, Kufa, Iraq

    Hasanen Alyasiri

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  1. Hasanen Alyasiri
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Correspondence to Hasanen Alyasiri .

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Editors and Affiliations

  1. National Advanced IPv6 Centre, Universiti Sains Malaysia, Penang, Malaysia

    Mohammed Anbar

  2. Hodeidah University, Hodeidah, Yemen

    Nibras Abdullah

  3. National Advanced IPv6 Centre, Universiti Sains Malaysia, Penang, Malaysia

    Selvakumar Manickam

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Alyasiri, H. (2021). Evolving Rules for Detecting Cross-Site Scripting Attacks Using Genetic Programming. In: Anbar, M., Abdullah, N., Manickam, S. (eds) Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol 1347. Springer, Singapore. https://doi.org/10.1007/978-981-33-6835-4_42

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  • DOI: https://doi.org/10.1007/978-981-33-6835-4_42

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6834-7

  • Online ISBN: 978-981-33-6835-4

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