The Internet of Things (IoT) is starting to integrate deeply into our daily lives thanks to the different services it provides. This technology has already made us more closely linked to the external environment through ubiquitous communication devices. However, even though this proximity has numerous benefits, it also has a significant security impact, where the cyber-attack surface has grown dramatically. In this regard, we present, in this paper, our results toward the development of a decision tree-based machine learning model for intrusion detection in Massive-IoT networks. The principal objective of this work is to provide a highly accurate detection model, while preserving resource consumption by developing a real prototype of the intrusion detection system. To this end, we first propose and apply our pre-processing methodology on the well-known Avast IoT-23 dataset, allowing us to reach a high detection rate with 99.99% of accuracy and just 1804KB of the model’s size. Then, we propose a new machine learning model based on the decision tree classifier and deploy it in a real environment with malicious attack traffic. Obtained results show that our proposed model allows 88% of real-traffic-based precision rate and up to 90% of specificity.