Deep learning based phishing web page detection

With the increase in the usage of e-commerce, social media and digital entertainment services, there is a tremendous increase in phishing activities. In this study, based on the observation of phishing activities, a study has been carried out to detect phishing websites with deep models and transfer learning. Within the scope of the study, a data set containing a total of 2852 screenshots, consisting of real and fake screenshots of websites such as adobe, amazon, apple and microsoft etc. was used. The results obtained by using transfer learning from AlexNet, VGG16 and RESNET50 models as well as the proposed multi- input CNN model were analyzed. Promising results are obtained from the experiments. The effects of the obtained findings on other future studies were discussed.