no code implementations • 10 Jan 2024 • Irfan Ozen, Karthika Subramani, Phani Vadrevu, Roberto Perdisci
SEShield consists of three main components: (i) a custom security crawler, called SECrawler, that is dedicated to scouting the web to collect examples of in-the-wild SE attacks; (ii) SENet, a deep learning-based image classifier trained on data collected by SECrawler that aims to detect the often glaring visual traits of SE attack pages; and (iii) SEGuard, a proof-of-concept extension that embeds SENet into the web browser and enables real-time SE attack detection.
no code implementations • 29 Aug 2017 • Yizheng Chen, Yacin Nadji, Athanasios Kountouras, Fabian Monrose, Roberto Perdisci, Manos Antonakakis, Nikolaos Vasiloglou
Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks.