Detection of Clickjacking using Convolutional Neural Network

Hariram, Kishore (2023) Detection of Clickjacking using Convolutional Neural Network. Masters thesis, Dublin, National College of Ireland.

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Abstract

A clickjacking attack is one of the most serious and dangerous vulnerabilities in modern web applications. The aim of clickjacking is for an attacker to trick the user or a victim to perform a malicious action or an activity by embedding a hidden iframe that is placed transparently over the webpage. This makes the attacker hijack a click from the victim without the victim’s knowledge. Even though clickjacking has gained much attention, it is still uncertain how and to what extent an attacker may use this practice to lure a victim and get personal information. This research, therefore, suggests a method for detecting malicious URLs that are susceptible to clickjacking attacks. This model uses Convolution Neural Network (CNN) technology to detect the suspicious iframe on a website, and HTML CSS property is utilized to highlight malicious iframe on the webpage. The “Dataset for Phishing website detection from the Data in Brief [1]” is used for the detection of a malicious iframe. The performance is evaluated by detecting the malicious iframe in minimal time with Convolution Neural Network (CNN) which results in good prediction of a malicious iframe.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Ayala-Rivera, Vanessa UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	28 Apr 2023 14:47
Last Modified:	28 Apr 2023 14:47
URI:	https://norma.ncirl.ie/id/eprint/6519

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