We are sorrounded by network-enabled embedded devices that transformed the Internet to the Internet-of-Things (IoT). Such embedded devices (e.g., wifi routers, IP cameras, smart home assistants, sensors and actuators, industrial controllers and gateways, head units in vehicles, EV chargers, ...) are all programmable computers that can be infected by malware (malicious software). This is not a pure theoretical threat: malware targeting such embedded IoT devices do exist in the wild (see e.g., Mirai). Malware empowers attackers to carry out large scale attacks, compromising millions of embedded devices in a short time and building large attack infrastructures (IoT botnets) to be used in DDoS attacks against Internet-based services. In addition, infecting embedded devices with malware in cyber-physical systems (e.g., vehicles, factories, ...) may lead to fatal physical accidents. So understanding how IoT malware works and evolves, and efficient detection of IoT malware on resource-constrained IoT devices are pressing issues and hot research topics. By choosing this topic, students can immerse themselves in the various aspects of dealing with IoT malware including:

• Effective and efficient malware detection on resource-constrained embedded devices (e.g., wifi routers, web cameras, smart home gadgets, robots, ...)
• Analysis of IoT malware samples using static and dynamic methods
• Collection, processing, and storage of IoT malware samples
• Analysis, clustering, and visualization of IoT malware datasets
• Studying the evolution of IoT malware families (genealogy)
• AI-based detection and creation of IoT malware