NORMA eResearch @NCI Library

Detection of Clickjacking Attacks using the Extreme Learning Machine algorithm

Patil, Yashodha (2020) Detection of Clickjacking Attacks using the Extreme Learning Machine algorithm. Masters thesis, Dublin, National College of Ireland.

[img]
Preview
PDF (Master of Science)
Download (931kB) | Preview
[img]
Preview
PDF (Configuration manual)
Download (1MB) | Preview

Abstract

Clickjacking attack is one of the emerging web-based attack. In clickjacking, the user is tricked to click on transparent iframes placed over the web elements. This may lead to unwanted operations without the user’s knowledge. Even though, clickjacking is one of the major points of discussion, it is still unclear, how and to what extent the attacker might use this attack to lure the user and gain the user data. Therefore, in this paper we have proposed a solution that identifies malicious links which are being used for clickjacking attacks. In this, Extreme Machine Learning (ELM) technique is used, that classifies the malicious links present on the webpage and these are displayed on the webpage using the HTML CSS property. Only one type of malicious link is identified i.e. phishing links present on the webpage. Hence, phishing dataset is used. The Extreme Learning Machine model was compared with the support vector machine learning model with respect to their performance metrics. The training time required by the ELM model is less compared to SVM model.

Item Type: Thesis (Masters)
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: Dan English
Date Deposited: 27 Jan 2021 18:00
Last Modified: 27 Jan 2021 18:00
URI: http://norma.ncirl.ie/id/eprint/4513

Actions (login required)

View Item View Item