MULTIPHISH: MULTI-MODAL FEATURES FUSION NETWORKS FOR PHISHING DETECTION

Phishing is an increasingly serious cybercrime. Phishers create phishing websites by mimicking legitimate websites to confuse users and steal their personal information. The proliferation of phishing websites and more advanced camouflage techniques are problems faced by most existing methods. In this paper, we propose a features fusion networks (MultiPhish) which is the first study on fusing multi-modal features with neural networks for the phishing detection task. In this end-to-end network, the domain and favicon of the website are represented via deep neural networks, and the representation of the website identity is obtained through multi-modal features fusion. In addition, the variation autoencoder (VAE) is introduced to optimize the representation. In the phishing detection module, we incorporate URL features to improve situations where phishing websites cannot be detected only by estimating whether the website identity is disguised. Based on the latest collected dataset, we have carried out extensive experiments and proved that our model is superior to the relevant methods. In addition, MultiPhish is a completely language-independent strategy, so it can perform phishing detection regardless of the text language.