is a central hub for research and collaboration activities in the context of innovative IoT-oriented smart systems and applications, which can also involve security and machine learning aspects. The goals of the thesis and internships with national and international companies will be to move from research to practice. Current topics of thesis and internship experiences include:

• Tiny Machine Learning
  1. TinyML for intrusion detection in resource-constrained devices.
  2. TinyML quantization/pruning to reduce footprint-memory.
  3. Federated learning for supporting security and privacy in embedded systems.
• Security in Microservices
  1. Vulnerability analysis in Container-based architectures.
  2. Advanced persistent threat (APT) in Container-based architectures.
  3. Anomaly detection in Container-based architectures.
• Intrusion Detection
  1. Anomaly detection in self-driving swarms (including vehicles, UAVs, mobile agents, etc.).
  2. Intrusion detection by resource constraining embedded (tiny) devices.
• Penetration Testing of Embedded Systems
  1. Vulnerability analysis of industrial systems and protocols.
  2. Vulnerability analysis of UAVs/drones.
  3. Vulnerability analysis of IoT network/systems.
  4. Vulnerability analysis of LoRa-based systems.
• Quantum Cryptography in Resource Constrained MCU Devices.