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Internet of Things (IoT) represents a massive deployment of connected, intelligent devices that communicate directly in private, public, and professional environments without human intervention. The increasing number and mobility make them more attractive to attackers. Therefore, many techniques have been integrated to secure IoT, such as authentication, availability, encryption, and data integrity. Intrusion detection systems (IDSs) are an effective security tool that can be enhanced using machine learning (ML) and deep learning (DP) algorithms. This paper presents an improved IDS using gradient boosting (GB) and decision tree (DT) through the open-source Catboost for IoT Security. The proposed model has been evaluated under the improved NSL- KDD, IoT-23, BoT-IoT, and Edge-IIoT datasets using the GPU to enhance the experimental setting. Compared with the well-existed IDS, the results prove that our …