Internet of Things boundary attacks must be monitored in near real-time to ensure the safety and security of critical infrastructure. In this research, we deliver an intelligent intrusion-finding scheme designed to identify assaults originating from the Internet of Things. In particular, a machine learning tactic called support vector machine (SVM) has been utilized to notice malicious IoT network traffic. The characteristics are derived using a 1D-CNN model, which stands for a convolutional neutral network. The hybrid whale dragonfly optimization method (H-WDFOA) is used to determine the best value for the SVM kernel weight. The identification solution guarantees safe operations and facilitates the interoperability of IoT connectivity protocols. When it comes to protecting a network, one of the most common forms of security equipment is an intrusion detection system (IDSs). In addition, 5G networks are under pressure to gather, analyze, and analyze enormous volumes of data traffic and network connections in order to meet the rising demand for user-centric cybersecurity solutions.