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Machine and deep learning-based XSS detection approaches: a systematic literature review

Web applications are paramount tools for facilitating services providing in the modern world. Unfortunately, the tremendous growth in the web application usage has resulted in a rise in cyberattacks. Cross-site scripting (XSS) is one of the most frequent cyber security attack vectors that threaten t...

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Main Authors: Thajeel, Isam Kareem, Samsudin, Khairulmizam, Hashim, Shaiful Jahari, Hashim, Fazirulhisyam
Format: Article
Language:English
Published: Elsevier 2023
Online Access:http://psasir.upm.edu.my/id/eprint/109487/1/1-s2.0-S1319157823001829-main.pdf
http://psasir.upm.edu.my/id/eprint/109487/
https://linkinghub.elsevier.com/retrieve/pii/S1319157823001829
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spelling my.upm.eprints.1094872024-11-06T01:41:44Z http://psasir.upm.edu.my/id/eprint/109487/ Machine and deep learning-based XSS detection approaches: a systematic literature review Thajeel, Isam Kareem Samsudin, Khairulmizam Hashim, Shaiful Jahari Hashim, Fazirulhisyam Web applications are paramount tools for facilitating services providing in the modern world. Unfortunately, the tremendous growth in the web application usage has resulted in a rise in cyberattacks. Cross-site scripting (XSS) is one of the most frequent cyber security attack vectors that threaten the end user as well as the service provider with the same degree of severity. Recently, an obvious increase of the Machine learning and deep learning ML/DL techniques adoption in XSS attack detection. The goal of this review is to come with a special attention and highlight of Machine learning and deep learning approaches. Thus, in this paper, we present a review of recent advances applied in ML/DL for XSS attack detection and classification. The existing proposed ML/DL approaches for XSS attack detection are analyzed and taxonomized comprehensively in terms of domain areas, data preprocessing, feature extraction, feature selection, dimensionality reduction, Data imbalance, performance metrics, datasets, and data types. Our analysis reveals that the way of how the XSS data is preprocessed considerably impacts the performance and the attack detection models. Proposing a full preprocessing cycle reveals how various ML/DL approaches for XSS attacks detection take advantage of different input data preprocessing techniques. The most used ML/DL and preprocessing stages have also been identified. The limitations of existing ML/DL-based XSS attack detection mechanisms are highlighted to identify the potential gaps and future trends. Elsevier 2023-06-20 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/109487/1/1-s2.0-S1319157823001829-main.pdf Thajeel, Isam Kareem and Samsudin, Khairulmizam and Hashim, Shaiful Jahari and Hashim, Fazirulhisyam (2023) Machine and deep learning-based XSS detection approaches: a systematic literature review. Journal of King Saud University - Computer and Information Sciences, 35 (7). pp. 1-24. ISSN 1319-1578 https://linkinghub.elsevier.com/retrieve/pii/S1319157823001829 10.1016/j.jksuci.2023.101628
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Web applications are paramount tools for facilitating services providing in the modern world. Unfortunately, the tremendous growth in the web application usage has resulted in a rise in cyberattacks. Cross-site scripting (XSS) is one of the most frequent cyber security attack vectors that threaten the end user as well as the service provider with the same degree of severity. Recently, an obvious increase of the Machine learning and deep learning ML/DL techniques adoption in XSS attack detection. The goal of this review is to come with a special attention and highlight of Machine learning and deep learning approaches. Thus, in this paper, we present a review of recent advances applied in ML/DL for XSS attack detection and classification. The existing proposed ML/DL approaches for XSS attack detection are analyzed and taxonomized comprehensively in terms of domain areas, data preprocessing, feature extraction, feature selection, dimensionality reduction, Data imbalance, performance metrics, datasets, and data types. Our analysis reveals that the way of how the XSS data is preprocessed considerably impacts the performance and the attack detection models. Proposing a full preprocessing cycle reveals how various ML/DL approaches for XSS attacks detection take advantage of different input data preprocessing techniques. The most used ML/DL and preprocessing stages have also been identified. The limitations of existing ML/DL-based XSS attack detection mechanisms are highlighted to identify the potential gaps and future trends.
format Article
author Thajeel, Isam Kareem
Samsudin, Khairulmizam
Hashim, Shaiful Jahari
Hashim, Fazirulhisyam
spellingShingle Thajeel, Isam Kareem
Samsudin, Khairulmizam
Hashim, Shaiful Jahari
Hashim, Fazirulhisyam
Machine and deep learning-based XSS detection approaches: a systematic literature review
author_facet Thajeel, Isam Kareem
Samsudin, Khairulmizam
Hashim, Shaiful Jahari
Hashim, Fazirulhisyam
author_sort Thajeel, Isam Kareem
title Machine and deep learning-based XSS detection approaches: a systematic literature review
title_short Machine and deep learning-based XSS detection approaches: a systematic literature review
title_full Machine and deep learning-based XSS detection approaches: a systematic literature review
title_fullStr Machine and deep learning-based XSS detection approaches: a systematic literature review
title_full_unstemmed Machine and deep learning-based XSS detection approaches: a systematic literature review
title_sort machine and deep learning-based xss detection approaches: a systematic literature review
publisher Elsevier
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/109487/1/1-s2.0-S1319157823001829-main.pdf
http://psasir.upm.edu.my/id/eprint/109487/
https://linkinghub.elsevier.com/retrieve/pii/S1319157823001829
_version_ 1814936570564706304
score 13.211869

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