Supply chains, network infrastructure, device compromise and protocol exploitation are prime targets of adversaries that can be exploited leading to a domino effect of compromised systems. This objective will facilitate trainees to comprehend the insidious threats coming from the integration of critical systems and to design more secure 5G-IoT and in general interconnected systems.

In particular, it will provide a controlled platform to simulate and study various adversarial techniques and strategies that can undermine the performance or integrity of AI algorithms as they are applied in
5G and IoT networks. Apart from increasing awareness for the dangers lurking from adversarial
AI, more importantly NITRO will enable the development and validation of AI models that are more
robust, secure, and capable of withstanding malicious attempts to manipulate the behaviour of the
models. As such the overarching goal is to enable researchers and practitioners to enhance their understanding of adversarial AI techniques and explore strategies for building more secure AI
systems.

It will serve as a critical resource for security practitioners, researchers, and organizations, enabling comprehensive tracking, analysis, and response to emerging threats and real-world incidents. By correlating this information, it will provide valuable insights into the evolving threat landscape of 5G-IoT interconnected networks and assist in devising effective mitigation strategies. Through its incident tracking and reporting capabilities, the NITRO repository facilitates incident response, forensics analysis, and the dissemination of best practices and lessons learned, fostering a proactive approach to maintaining the security and resilience of 5G-IoT environments.

 In that fashion, demonstrations will validate every aspect of the NITRO Platform and its components and will lay the foundations for even better analysis, exploitation and adoption of the NITRO key results