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The Internet of Things (IoT) has continued gaining in popularity and importance in everyday life in recent years. However, due to the number of sensitive and private data produced by IoT systems, IoT networks become the new privileged targets for cyberattackers. In addition, these devices are constrained in terms of ressources and energy. Therefore, due to their characteristics, security systems developed for IoT networks are an open question in research. At the same time, Machine Learning (ML) has gained a phenomenal success in various fields like telecommunications, transport or cybersecurity. Nonetheless, the application of ML can cause significant damage when put in the hands of an attacker. The increasing use of ML in creating attacks is significantly changing the threat landscape in two notable ways. The first will be the expansion of existing attacks. Indeed, by integrating ML algorithms in current cyberattacks, these will become more resistant, more reactive, and less recognizable by existing detection methods. The second will represent the creation of new threats which, until then, were not achievable due to their massive demand for data or their excessive manual processing time.