In recent years, we have experienced rapid and beneficial development of IoT solutions throughout all aspects of life. In addition to the apparent advantages, the increased number and variety of devices have resulted in more 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. Because IoT devices are typically simple and have few computing resources, it puts them at risk of being infected and attacked. IDS intrusion detection systems are considered superior 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 performance optimization. Our experiment carried out on the IoT-23 dataset shows that the proposed model's accuracy and performance for DDoS attack detection are better than those proposed by other previous research