- author = "Hasanen Alyasiri",
- title = "Evolving Rules for Detecting Cross-Site Scripting Attacks Using Genetic Programming",
- booktitle = "2nd International Conference on Advances in Cyber Security, ACeS 2020",
- year = "2020",
- editor = "Mohammed Anbar and Nibras Abdullah and Selvakumar Manickam",
- volume = "1347",
- series = "Communications in Computer and Information Science",
- pages = "642--656",
- address = "Penang, Malaysia",
- month = dec # " 8-9",
- publisher = "Springer",
- note = "Revised Selected Papers",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-981-33-6834-7",
- timestamp = "Mon, 15 Feb 2021 12:59:21 +0100",
-
biburl = "
https://dblp.org/rec/conf/aces/Alyasiri20.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
-
URL = "https://link.springer.com/chapter/10.1007/978-981-33-6835-4_42",
-
DOI = "
doi:10.1007/978-981-33-6835-4_42",
- 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 8600 bytes.",
-
notes = "Also known as
\cite{DBLP:conf/aces/Alyasiri20}
Department of Computer Science, University of Kufa, Kufa, Iraq",
