Detecting phishing attacks using a combined model of LSTM and CNN

Phishing, a social engineering crime which has been existing for more than two decades, has gained significant research attention to find better solutions to face against the very dynamic strategies of phishing. The financial sector is the primary target of phishing, and there are many different approaches to combat phishing attacks. Software-based detection approaches are more prominent in phishing detection; however, still, there is no robust solution that can stable for a long period. The primary purpose of this paper is to propose a novel solution to detect phishing attacks using a combined model of LSTM and CNN deep networks with the use of both URLs and HTML pages. The URLs are learned using an LSTM network with 1D convolutional, and another 1D convolutional network is used to learn the HTML features. These two networks were trained separately and combined through a sigmoid layer by dropping the last layer of each model to have the proposed model. The proposed model reached 98.34% in terms of accuracy, and that is above the previously recorded highest accuracy of 97.3% among the detection models used both URL and HTML features in the explored literature. The solution requires feature extraction only with HTML pages, and URLs were directly fed with a minimum pre-processing. Although the proposed solution uses extracted HTML features, those do not depend on third-party services. Therefore, an efficient real-time application can be implemented using the proposed model to detect phishing attacks to safeguard Internet users.