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The Exponential Growth Of Internet-connected Devices And Online Services Has Amplified The Frequency And Sophistication Of Distributed Denial-of-Service (DDoS) Attacks, Posing Severe Threats To Network Availability And Reliability. Traditional Rule-based And Signature-driven Detection Systems Often Fail Against Evolving Attack Patterns, While Conventional Machine Learning Models Struggle To Generalize In Highly Dynamic Traffic Environments. Deep Learning Techniques Have Recently Shown Promise In Extracting Complex Traffic Patterns For Accurate Anomaly Detection; However, Their Performance Is Often Hindered By Issues Such As High False-positive Rates And Vulnerability To Adversarial Manipulation. To Address These Challenges, This Study Proposes A Deep Learning-based DDoS Detection Framework Enhanced With Adversarial Optimization, Which Simultaneously Improves Detection Robustness And Adaptability. The Proposed Method Leverages Deep Neural Architectures For Feature Representation And Employs Adversarial Optimization Strategies To Refine Model Parameters, Enhance Resilience Against Evasion Attempts, And Minimize Classification Errors. Experimental Evaluation On Benchmark Network Traffic Datasets Demonstrates That The Framework Achieves Superior Detection Accuracy, Robustness To Adversarially Crafted Traffic, And Reduced False Alarms Compared To Baseline Models. This Work Highlights The Potential Of Integrating Deep Learning With Adversarial Optimization As A Practical And Scalable Solution For Next-generation DDoS Attack Detection In Complex Network Environments.