Identifying malware families of IoT botnets by machine learning using their network behavior

In this project, we developed a tool called NURSE that can detect malware infections on Internet of Things (IoT) devices. We also studied the security risks posed by remote management devices and developed a tool to secure them against unauthorized access. Additionally, we analyzed the supply and demand for Cybercrime-as-a-Service (CaaS) on a cybercrime forum, finding that only a fraction of known CaaS types were being offered and there was little change over time. The project has contributed to understanding and addressing the security risks of IoT devices, CaaS networks, and remote management devices.