GlyphNet: Homoglyph Domain Detection with CNNs
Paper · Code · Dataset Authors: Akshat Gupta, Laxman Singh Tomar, Ridhima Garg Research overview GlyphNet studies visual detection of homoglyph domains using an attention-based CNN and a dataset of real and synthetically altered domain images. The paper reports 0.93 accuracy, F1 and AUC on its own test set. These results describe the paper experiment; the separately rendered public release is detailed under Paper dataset and public release. Introduction In cyber security, attackers employ different attacks to infiltrate our systems and networks, with the objective varying from stealing crucial information to inflicting system damage. One such deceptive attack is the homoglyph attack, which involves an attacker trying to fool humans and computer systems by using characters and symbols that may appear visually similar to characters used in real domain and process names but are different. ...