In the evolving landscape of network security, Distributed Denial of Service (DDoS) attacks pose a significant threat to the integrity and availability of online services globally. With increasing complexity in network infrastructure, the ability to mitigate and recover from such attacks is paramount. This paper introduces a novel simulation model designed to dynamically replicate DDoS attacks and assess the efficacy of corresponding recovery strategies within a controlled network environment. Leveraging real-time feedback mechanisms, the model adapts both attack and recovery processes, providing a granular view of network behavior under stress. Key nodes identified based on strategic importance and vulnerability are targeted while recovery mechanisms dynamically adjust to mitigate the impact on critical services. The dual simulation of attack and recovery processes concurrently offers deep insights into network interdependencies, particularly in the context of cascading failures. Our extensive simulation runs reveal critical insights into the timing and effectiveness of response strategies, highlighting potential improvements in resilience and recovery speed.