The large-scale deployment of modern phishing attacks relies on the automatic exploitation of vulnerable websites in the wild, to maximize profit while hindering attack traceability, detection and blacklisting. To the best of our knowledge, this is the first work that specifically leverages this adversarial behavior for detection purposes. We show that phishing webpages can be accurately detected by highlighting HTML code and visual differences with respect to other (legitimate) pages hosted within a compromised website. Our system, named DeltaPhish, can be installed as part of a web application firewall, to detect the presence of anomalous content on a website after compromise, and eventually prevent access to it. DeltaPhish is also robust against adversarial attempts in which the HTML code of the phishing page is carefully manipulated to evade detection. We empirically evaluate it on more than 5,500 webpages collected in the wild from compromised websites, showing that it is capable of detecting more than 99% of phishing webpages, while only misclassifying less than 1% of legitimate pages. We further show that the detection rate remains higher than 70% even under very sophisticated attacks carefully designed to evade our system.

We present PharmaGuard, a novel system for the automatic discovery of illegal online pharmacies, aimed at assisting law-enforcement toward their early identification, blacklisting and shutdown. Given a previously labelled set of examples, the system is able to learn a profile of (illegal) pharmacies, and then exploit it to discover never-before-seen instances indexed by popular web search engines. Our experiments, performed on webpages found in the wild, indicate that our approach is lightweight, allows for high accuracy and can substantially complement state-of-the-art blacklists. We also present a report on the detected online pharmacies that better highlights the relevance of this threat for Internet users

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