From GenAIs to RGenAIs: A Contextual Modelling of Potential Network Attacks within IoT Networks
Akintunde Ayodeji Alonge · 2025
There is huge potential for artificial intelligence (AI)-driven attacks on Internet of Things (IoT) networks, particularly with the growing use of generative AI (GenAI) and regenerative AI (RGenAI). GenAIs are known for generating new datasets with similar attributes to their underlying trained datasets, while RGenAIs can reshape and adapt the inputs feeding GenAIs based on feedback responses. These AI technologies, when combined, can inflict massive costs on IoT networks that may be difficult to detect and resolve. In this paper, a contextual model is proposed to examine the possible scenarios of IoT network attacks arising from the combination of GenAI/RGenAI entities. This model constructs potential AIdriven attacks on IoT ecosystems as a looped process occurring in three phases: learn, deploy, and adapt. The paper further considers the likely use cases of this proposed model and provides insights into possible countermeasures to prevent such AI attacks.