In recent years, we have experienced the rapid and beneficial development of IoT solutions throughout all aspects of life. In addition to the obvious advantages, the increase in the number and variety of devices has resulted in more challenges on security issues. The DDOS attack, which originates from a broad range of sources and is a significant challenge for IoT systems, is one of the most prevalent but devastating attacks. IoT devices are typically simple and have few computing resources, putting them at risk of being infected devices and attackers. IDS intrusion detection systems are regarded to be the foremost line of protection against DDOS attacks. Therefore, the IDS system attracts many researchers and implements intelligent techniques such as machine learning and fuzzy logic to detect these DDOS attacks quickly and precisely. Along with the approach of intelligent computation, this study presents a novel technique for detecting DDOS attacks based on hedge algebra, which has never been implemented on IDS systems. We use the PSO swarm optimization algorithm to optimize the proposed model’s parameters for optimized performance. Our experiment on the IoT-23 dataset shows that the proposed model’s accuracy and performance metrics for DDOS attack detection are better than those proposed by other previous authors.