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Convolutional Neural Networks and Long Short Term Memory for Phishing Email Classification
Journal article   Open access   Peer reviewed

Convolutional Neural Networks and Long Short Term Memory for Phishing Email Classification

Regina Eckhardt and Sikha S Bagui
International journal of computer science and information security, Vol.19(5), 02 Paper 01052109
05/2021
DOI: https://doi.org/10.5281/zenodo.4898109

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Abstract

The focus on this work is on classifying phishing emails using deep neural networks. Since phishing emails have no specific characteristic, they are difficult to detect and classify, and little research has been done on the detection of phishing emails. In this work, two deep neural networks, Long Short Term Memory (LSTM), a form of Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), were compared and used for classification of phishing emails. RNN is the most used neural network for text classification. CNNs have also shown to be effective in text classification. In addition to tuning hyperparameters, different activation functions and optimizers are used for comparing the performance of CNN and LSTM on the basis of accuracy and the ROC-score. LSTM achieved a higher accuracy than CNN, and overall the Adam Optimizer performed better than the SGD optimizer. The best parameters for higher accuracy and ROC-score are also presented.
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