Abstract
This research investigates the Distributed Denial-of-Service (DDoS) protection model, focusing on flooding attacks, where attackers overwhelm a server with excessive requests to degrade its processing capabilities. Unlike traditional approaches that merely aim to mitigate the impact of DDoS attacks, our study emphasizes developing robust protection models to safeguard against such threats. We introduce a novel protection strategy that incorporates rate-limiting algorithms to control the influx of requests, ensuring that only legitimate traffic reaches the server. Further, we explore packet filtering based on valid Time-to-Live (TTL) values, coupled with innovative packet scheduling techniques: including First-Come, First-Served (FCFS) and Priority Queue methodologies to enhance server responsiveness and efficiency. Through simulations our findings reveal significant improvements in server performance under DDoS attack conditions, evidenced by reduced packet drop rates and improved response times. The successful implementation of these protection models demonstrates their potential in securing networks against the disruptive effects of DDoS attacks, offering a promising direction for future research in cybersecurity.