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Phishing Detection Using Convolutional Neural Network and ADADELTA

Phade, Tejas Umakant (2020) Phishing Detection Using Convolutional Neural Network and ADADELTA. Masters thesis, Dublin, National College of Ireland.

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The internet has been integral and indispensable part of people’s life to do mundane tasks or to communicate personal/sensitive information or to use educational, medical, financial services. Since there is an influx of inexperienced users, the internet brings serious security problems and these vulnerabilities are exploited by attackers. Novice users are naıve and phishers use phoney web pages to lure and deceive them resulting into gaining their information which has costed users a lot of their fortune; this technique is known as Phishing. Decades have been devoted in developing novel technique to detect phishing website. Even though superior performance can be achieved through state-of-the-art solutions, it demands substantial amount of manual engineering and does not provide guaranteed results. In this study, we focus on design and development of phishing detection solution based on deep learning, leveraging Convolutional Neural Network (CNN) and ADADELTA algorithm. The developed solution is a pure image-based approach with addition of similarity detection has been taken to avoid non-text phishing tricks such as HTML Contents or Flash objects. Accuracy of 96% is shown with the proposed 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 18:05
Last Modified: 27 Jan 2021 18:05

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