Malware Detection Among Contact Tracing Apps with Deep Learning

Contact tracing has built into a cost-effective social tool, complementary for the prevention and containment of the coronavirus and similar pandemics. Especially these days that meningitis, influenza, but also streptococcus and respiratory syncytial virus (RSV) are on the rise and affect vulnerable populations, e.g. young children, whose immune system is unprepared due to lack of exposure to common viruses during the recent pandemic, contact tracing apps can be used to detect behavioural trends of the users and contribute to outbreak management. This work suggests a comprehensive framework for the identification of malware within contact tracing applications, leveraging deep learning technology, and experimentally corroborates its efficiency. We also introduce a safe and efficient retrieval mechanism for apps associated with the Sustainable Development Goal (SDG) 3 associated with communicable diseases. We aspire to contribute to the safe dissemination of tracing apps in the era of contagious viruses.