Peculiarities of phishing threats and preventive measures in the conditions of war in Ukraine

The paper is devoted to the study of the peculiarities of phishing attacks on the personnel of enterprises and institutions of Ukraine during the war period (from February 2022- till now). The life cycle of the most popular attacks is analyzed. The focus is made on email phishing, which is the most popular for attacks on enterprises. A list of typical topics of phishing emails, psychological vectors of phishing influence, typical for attacks on Ukrainian users, additional factors that contribute to the success of attacks have been revealed. A countermeasures for phishing attacks prevention have been recommended. A list of phishing keywords was collected and templates were developed, a software solution based on artificial intelligence approaches was proposed to automate the generation of phishing letters in Ukrainian that can be used during "false alarms" and staff training in large enterprises

Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models

Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against it. Given the prevalence of fraud in the healthcare domain, it is important to consider the adversarial use of DNNs in manipulating sensitive data that is crucial to patient healthcare. In this work, we present the design and implementation of a DNN-based image translation attack on biomedical imagery. More specifically, we propose Jekyll, a neural style transfer framework that takes as input a biomedical image of a patient and translates it to a new image that indicates an attacker-chosen disease condition. The potential for fraudulent claims based on such generated 'fake' medical images is significant, and we demonstrate successful attacks on both X-rays and retinal fundus image modalities. We show that these attacks manage to mislead both medical professionals and algorithmic detection schemes. Lastly, we also investigate defensive measures based on machine learning to detect images generated by Jekyll.Comment: Published in proceedings of the 5th European Symposium on Security and Privacy (EuroS&P '20