Research of the Capabilities of Deep Learning Algorithms to Protection Against Phishing Attacks

Sofia P. Korniukhina, Olga R. Laponina

Abstract


Phishing is one of the most common threats on the Internet which is why the development of effective protection methods is an extremely important task. This article discusses works that use the capabilities of machine and deep learning algorithms to protect against phishing attacks, as well as developed the comparison criteria and carried out the comparative analysis of solutions. The comparison of protection systems against phishing attacks was carried out according to the following criteria: the type of analyzed elements (HTML, URL, CSS); the dataset preprocessing methods (normalization and feature selection); the required sample size; the ML/DL algorithms used to detect phishing attacks; the number of errors of the 1st and 2nd kind, the quality criteria of the model. In the works CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) are most frequently studied, both separately and in combination with each other. Also, the SVM (Support Vector Machine) and DT (Decision Tree) algorithms, which are used for classification problems, are often studied.

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