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Multiclass Classification of XSS Web Page Attack using Machine Learning Techniques

by S. Krishnaveni, K. Sathiyakumari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 12
Year of Publication: 2013
Authors: S. Krishnaveni, K. Sathiyakumari
10.5120/12940-0033

S. Krishnaveni, K. Sathiyakumari . Multiclass Classification of XSS Web Page Attack using Machine Learning Techniques. International Journal of Computer Applications. 74, 12 ( July 2013), 36-40. DOI=10.5120/12940-0033

@article{ 10.5120/12940-0033,
author = { S. Krishnaveni, K. Sathiyakumari },
title = { Multiclass Classification of XSS Web Page Attack using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 12 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number12/12940-0033/ },
doi = { 10.5120/12940-0033 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:42:07.481108+05:30
%A S. Krishnaveni
%A K. Sathiyakumari
%T Multiclass Classification of XSS Web Page Attack using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 12
%P 36-40
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Web applications are most widely used technique for providing an access to online services. At the same time web applications are easiest way for vulnerable acts. When a security mechanism is failed then the user may download malicious code from a trusted web site. In this case, the malicious script is contracted to full access with all assets belonging to that legitimate web site. These types of attacks are called Cross-Site Scripting (XSS) attacks. Cross Site Scripting (XSS) attacks are the most common type of attack against web application, which allows hackers to inject the malicious script code for stealing the user's confidential information. Recent studies show that malicious code detection has become the most frequent vulnerability. In web browsers, the malicious script codes are executed and used to transfer the sensitive data to the third party (or hackers) domain. Currently, most research areas are attempted to prevent XSS on both the client and server side. In this paper, we present a machine learning technique to classify the malicious web pages. This work focus some of the possible ways to detect the XSS script on client side based on the features extracted from the web document content and the URL to scan the web pages for check the malicious scripts.

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Index Terms

Computer Science
Information Sciences

Keywords

Cross site scripting (XSS) SOL injection Script injection attact Cross site Request Forgery (CSRF) vulnerability malicious code

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