DDoS attacks present a significant danger to computer networks, as they can cause disruption and deny access to legitimate users. Although various techniques have been proposed to detect and prevent jamming attacks, there is a need for more efficient solutions to tackle DDoS attacks. This work presents a taxonomy of different DDoS attacks in the Internet of Things (IoT) network and proposes a solution for replay attacks. Using Unified Modeling Language (UML) based activity modeling, all variations of DDoS attacks are modeled to understand their behavior and performance in the environment. This knowledge is used to develop a Threshold Based Countermeasure for replay attacks, allowing the attack inside the network and blocking it after detecting its behavior. Simulation results under realistic conditions prove the effectiveness of the proposed work concerning energy, time, and computational cost. Furthermore, we compare the proposed work with the currently available models and the results show that our proposed work outperforms the other solutions in terms of the above-mentioned performance parameters, leading to enhanced network performance. Therefore, the proposed solution provides a more efficient and effective method for detecting and preventing DDoS attacks in the IoT environment