Phishing Detection Using Computer Vision

Abstract

Phishing is a cyber-crime wherein innocent web users are trapped into a counterfeit website, which is visually similar to its legitimate counterpart, but in reality, it is fake. Initially, users are redirected to phishing websites through various social and technical routing techniques. Users being ignorant about the illegitimacy of the website, provide their personal information such as user id, password, credit card details, bank account details to name a few. These details are stolen by the phishers and later used for either financial gains, or to tarnish a brand image or even more grave crimes like identity theft. Many phishing detection and prevention techniques are proposed in the literature; however, there is much scope in the cyber-security world with the advent of smart machine learning and deep learning methods. In this research, we explored computer vision techniques and build deep learning and machine learning classifiers to detect phishing website and their brands. Some of the experiments include Transfer Learning and Representation Learning techniques by utilizing various off-the-shelf Convolutional Neural Network (CNN) architectures to extract image features. It is observed that DenseNet201 outperforms all experiments conducted as well as the existing state-of-the-art on the dataset used, proving the hypothesis that Convolutional neural networks are an effective solution for extracting relevant features from phishing webpages for phishing detection classification.KeywordsPhishing detectionDeep learning in cyber-securityAnti-phishing mechanismWebsite brand predictionImage classificationTransfer learningRepresentation learningComputer vision