DEEP LEARNING APPROACHES FOR DDOS ATTACK DETECTION AND MITIGATION IN CLOUD COMPUTING ENVIRONMENTS: A COMPREHENSIVE SURVEY AND NOVEL FRAMEWORK

Cloud computing environments face unprecedented security challenges from sophisticated Distributed Denial of Service (DDoS) attacks that threaten service availability and business continuity. Traditional signa‐ ture-based and threshold-driven detection mechanisms demonstrate inadequate performance against modern at‐ tack vectors, necessitating intelligent adaptive solutions. This research presents an innovative deep learning methodology for enhanced DDoS attack detection and mitigation specifically tailored for cloud infrastructures. We introduce the Intelligent Multi-Layered Defense System (IMLDS), a novel framework integrating ad‐ vanced neural architectures including Graph Convolutional Networks, Transformer-based attention mecha‐ nisms, and Federated Learning approaches. Our comprehensive experimental analysis across diverse datasets demonstrates exceptional performance metrics: 99.2% detection accuracy, 0.15% false positive rate, and real- time processing capabilities handling 750,000 network flows per second. The proposed system successfully identifies zero-day attack variants, adapts to evolving threat patterns through continuous learning, and main‐ tains operational efficiency under extreme traffic loads exceeding 150 Gbps. Implementation studies in produc‐ tion cloud environments validate practical effectiveness with 34% reduction in attack response time and 89% decrease in false alarms compared to existing solutions. This work contributes novel architectural innovations, comprehensive threat taxonomy for cloud environments, and practical deployment strategies that advance the state-of-the-art in cybersecurity defense mechanisms.