A Combinational Approach of Hybrid Model BiLSTM-CNN-GRU to Improve the Detection rate of Click jacking in Websites

Thirunavukarasu, Udhaya (2023) A Combinational Approach of Hybrid Model BiLSTM-CNN-GRU to Improve the Detection rate of Click jacking in Websites. Masters thesis, Dublin, National College of Ireland.

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Abstract

The growing incidence of clickjacking attacks poses a severe threat to web security. Clickjacking is still a concern today because of how dynamic it is and how well it can get around traditional defenses. Despite earlier studies in this area, there is still much to be learned about clickjacking hits because it is unclear how far the attack can go in gathering the victim's personal information. The primary focus is to provide a more accurate and efficient detection method to detect attacks. In order to identify clickjacking attempts with fewer false positives and false negatives, So, BILSTM hybrid layer of CNN+GRU technique is used, that classifies the malicious Phishing content present on the webpage and will be highlighted. This hybrid deep learning model was compared with Convolutional Neural Network (CNN) model with respect to their performance metrics like Accuracy and statistical calculation of Specificity and Sensitivity metrics. The results demonstrate the BILSTM model's enhanced skills in distinguishing between benign and phishing URLs, providing a significant breakthrough in clickjacking detection.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Vikas 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 > Algebra > Algorithms > Computer algorithms Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	25 Apr 2025 10:48
Last Modified:	25 Apr 2025 10:48
URI:	https://norma.ncirl.ie/id/eprint/7471

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