ADVANCED HYBRID GENERATIVE AI MODELS FOR MULTI-LAYERED DETECTION AND DEFENSE AGAINST DDOS ATTACKS

DDoS attacks remain a critical threat to organizations, disrupting services and inflicting serious financial and reputational damage. Traditional defenses in the form of rule-based systems and statistical models often cannot keep up with the sophistication and evolution of such attacks. This paper introduces a hybrid GenAI framework, designed to address these challenges by combining the adaptive capabilities of advanced generative models with the robustness of traditional rule-based systems. The proposed multi-layered architecture detects and analyzes anomalous network traffic patterns indicative of DDoS attacks using Generative Adversarial Networks (GANs), autoencoders, and transformers. GANs are utilized for generating realistic attack scenarios for the training and validation of models in order to enhance the robustness of the detection system. Autoencoders identify very subtle anomalies in network traffic due to reconstruction errors, whereas transformers process sequential traffic data in order to capture long-term dependencies and detect coordinated attack behaviors. These advanced generative techniques are integrated with rule-based defenses that apply predefined thresholds, traffic filtering, and IP blacklisting for immediate response to known attack vectors. To combine the strengths of both layers, a decision fusion module is proposed, which integrates insights from generative models and rule-based systems using weighted scoring and logical decision trees. This hybrid approach enhances the detection accuracy of DDoS attacks, minimizes false positives, and ensures prompt response to threats. Thorough experiments on real-world DDoS datasets supplemented with synthetic data generated by GANs demonstrate the superior performance of the proposed framework in detecting and mitigating a wide range of DDoS attacks. Results show a sharp increase in detection rates with a reduction in false positives along with mitigation times that have improved compared to traditional methods. Moreover, the system demonstrates adaptability to evolutionary attack patterns, which signifies its feasibility for real-world deployment into dynamic network environments. Coupling state-of-the-art generative AI techniques with mature defense mechanisms, this framework embodies a new benchmark for resilient and scalable, yet intelligent, DDoS mitigation.