DDoS Protection System for Cloud using AWS and Machine Learning

With the increased reliance on cloud computing, it has emerged as the most critical target of Distributed Denial of Service attacks that threaten availability, performance, and reliability of applications hosted in clouds. This paper introduces DDoS Protection System for Cloud using AWS and Machine Learning-a comprehensive and adaptive solution for real-time detection and mitigation of DDoS attacks. The proposed framework exploits the scalability of AWS cloud services as well as the intelligence of the machine-learning algorithms to support advanced traffic analysis in order to distinguish between legitimate users and malicious entities. The system integrates anomaly-based detection techniques with dynamic resource allocation as well as automated mitigation workflows to handle high volumes of complex attack patterns in a manner that minimizes latency in operational activities. Real-time alerting, smooth scalability, and efficient usage of cloud resources make the solution practical for dynamic environments of clouds. The experimental evaluations clearly indicate that the effectiveness of neutralizing various vectors of DDoS attacks and maintaining high availability while optimizing resource consumption. With cloud-native tools and intelligent analytics together, this research work positively contributes to the advancement of cloud security and lays foundational work in hybrid and multi-cloud architectures. Therefore, such research findings provide insight for an organization seeking to develop its defenses against the rising cyber threats.