Cross-site detection system using Support Vector Machine / Ahmad A’limmuddin Bahrim

A form of security issue called cross-site scripting (XSS) enables attackers to insert malicious code into a website. When a user accesses the website, the malicious code may steal personal data or carry out other undesirable actions. XSS attacks can be classified as stored, reflected, or DOM-based....

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Main Author:	Bahrim, Ahmad A’limmuddin
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Cryptography Access control Computer security
Online Access:	https://ir.uitm.edu.my/id/eprint/96294/1/96294.pdf
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id	my-uitm-ir.96294
record_format	uketd_dc
spelling	my-uitm-ir.962942024-06-04T07:20:24Z Cross-site detection system using Support Vector Machine / Ahmad A’limmuddin Bahrim 2023 Bahrim, Ahmad A’limmuddin Cryptography. Access control. Computer security A form of security issue called cross-site scripting (XSS) enables attackers to insert malicious code into a website. When a user accesses the website, the malicious code may steal personal data or carry out other undesirable actions. XSS attacks can be classified as stored, reflected, or DOM-based. With the help of machine learning techniques like Support Vector Machines (SVM), these attacks, which are frequent, can be stopped. A cross-site detection system for XSS scripting was created in this work utilising the Support Vector Machine (SVM) technique. Support Vector Machine (SVM) is a technique used to determine whether XSS scripts have been implanted in a website or not. Six different research approaches, including a preliminary study, requirement analysis, data gathering, design, implementation, evaluation, and documentation, were used to construct this system efficiently. The system's stated objectives could be successfully attained at the end of the study thanks to the tight alignment of these approaches with those goals. Next, the dataset used for this study is dataset named “Cross site scripting XSS dataset for Deep learning” can be download from website online which is Kaggle contributed by Syed Saqlain Hussain Shah. The dataset contains Cross site scripting attack (XSS) data along with benign data. The research is significant in addressing the serious threats posed by cross-site assaults to the security and integrity of web systems, and in contributing to the development of effective detection and mitigation strategies. 2023 Thesis https://ir.uitm.edu.my/id/eprint/96294/ https://ir.uitm.edu.my/id/eprint/96294/1/96294.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Ramlan, Muhammad Atif
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ramlan, Muhammad Atif
topic	Cryptography Access control Computer security
spellingShingle	Cryptography Access control Computer security Bahrim, Ahmad A’limmuddin Cross-site detection system using Support Vector Machine / Ahmad A’limmuddin Bahrim
description	A form of security issue called cross-site scripting (XSS) enables attackers to insert malicious code into a website. When a user accesses the website, the malicious code may steal personal data or carry out other undesirable actions. XSS attacks can be classified as stored, reflected, or DOM-based. With the help of machine learning techniques like Support Vector Machines (SVM), these attacks, which are frequent, can be stopped. A cross-site detection system for XSS scripting was created in this work utilising the Support Vector Machine (SVM) technique. Support Vector Machine (SVM) is a technique used to determine whether XSS scripts have been implanted in a website or not. Six different research approaches, including a preliminary study, requirement analysis, data gathering, design, implementation, evaluation, and documentation, were used to construct this system efficiently. The system's stated objectives could be successfully attained at the end of the study thanks to the tight alignment of these approaches with those goals. Next, the dataset used for this study is dataset named “Cross site scripting XSS dataset for Deep learning” can be download from website online which is Kaggle contributed by Syed Saqlain Hussain Shah. The dataset contains Cross site scripting attack (XSS) data along with benign data. The research is significant in addressing the serious threats posed by cross-site assaults to the security and integrity of web systems, and in contributing to the development of effective detection and mitigation strategies.
format	Thesis
qualification_level	Bachelor degree
author	Bahrim, Ahmad A’limmuddin
author_facet	Bahrim, Ahmad A’limmuddin
author_sort	Bahrim, Ahmad A’limmuddin
title	Cross-site detection system using Support Vector Machine / Ahmad A’limmuddin Bahrim
title_short	Cross-site detection system using Support Vector Machine / Ahmad A’limmuddin Bahrim
title_full	Cross-site detection system using Support Vector Machine / Ahmad A’limmuddin Bahrim
title_fullStr	Cross-site detection system using Support Vector Machine / Ahmad A’limmuddin Bahrim
title_full_unstemmed	Cross-site detection system using Support Vector Machine / Ahmad A’limmuddin Bahrim
title_sort	cross-site detection system using support vector machine / ahmad a’limmuddin bahrim
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/96294/1/96294.pdf
_version_	1804889983753912320

Cross-site detection system using Support Vector Machine / Ahmad A’limmuddin Bahrim

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