Deep Learning Phishing Email Classifier Combined with NLP

Ebong, Maurice Aniefiok (2022) Deep Learning Phishing Email Classifier Combined with NLP. Masters thesis, Dublin, National College of Ireland.

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Abstract

Given the fact that phishing email attacking techniques are constantly being developed and updated, the current methods are inadequate to tackle the issue. Additionally, the increase in the number of attacks mostly suggests that there is a need for the development of robust techniques in tackling phishing email attacks. One primary concern with phishing email detection is that the current phishing detection technique cannot adapt to the ever-changing methods and semantics used by phishers (attackers) against their victims. In this research work, two machine learning techniques namely Support Vector Machine (SVM) and Random Forest (RFC) and a Deep learning technique namely Deep Neural Network (DNN) had been used to classify phishing emails. NLP word2vec technique was applied to the dataset and resampling techniques were also applied to the dataset to handle the imbalance in the dataset. The results obtained from the models implemented indicate that SVM, RFC, and DNN have 100% accuracy in classifying phishing emails and recorded a training and testing time for models are 133.3s and 0.21s, 943.70s and 0.09s, 436.85s and 2.29s respectively.

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 Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	19 Dec 2022 15:46
Last Modified:	07 Mar 2023 17:25
URI:	https://norma.ncirl.ie/id/eprint/6000

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