A Phishing Webpage Detection Method Based on Stacked Autoencoder and Correlation Coefficients

Jian Feng, Lianyang Zou, Tianzhu Nan


Phishing is a kind of cyber-attack that targets naive online users by tricking them into revealing sensitive information. There are many anti-phishing solutions proposed to date, such as blacklist or whitelist, heuristic-based and machine learning-based methods. However, online users are still being trapped into revealing sensitive information in phishing websites. In this paper, we propose a novel phishing webpage detection model, based on features that are extracted from URL, source codes of HTML, and the third-party services to represent the basic characters of phishing webpages, which uses a deep learning method – Stacked Autoencoder (SAE) to detect phishing webpages. To make features in the same order of magnitude, three kinds of normalization methods are adopted. In particular, a method to calculate correlation coefficients between weight matrixes of SAE is proposed to determine optimal width of hidden layers, which shows high computational efficiency and feasibility. Based on the testing of a set of phishing and benign webpages, the model using SAE achieves the best performance when compared to other algorithms such as Naive Bayes (NB), Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). It indicates that the proposed detection model is promising and can be applied effectively to phishing detection.


phishing, deep learning, correlation coefficient

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