With the growing prevalence of highly sophisticated and frequent cyber-attacks aimed at the infrastructure and, in particular, at the Internet of Things (IoT) devices, more intelligent, high-level intrusion detection methodologies are required. Standard security measures such as encryption and authentication are not enough to detect new or advanced threats like zero-day attacks and DDoS attacks. In this paper, we propose a Hybrid IDS architecture for the development of a rule-based IDS based on ML and DL mechanisms in order to improve cybersecurity in IoT and WSN networks. The system uses Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and anomaly detection to reliably and timely characterize new and known threats. Experimental results on three benchmark data sets, namely, NSL-KDD, DS2OS, and IoT Botnet, show that the Hybrid IDS could have an overall detection accuracy of 96.4% and outperformed traditional IDS models in terms of various performance measures, such as precision, recall, and F1-score. The experimental results at least show the system's capability and flexibility to defend against various types of threats, and pose a practical and scalable solution for real-world IoT security challenges.