AI for Detecting and Mitigating Distributed Denial of Service (DDoS) Attacks in Cloud Networks

Distributed Denial of Service (DDoS) attacks pose a significant threat to cloud networks, disrupting services, degrading performance, and causing financial losses. Traditional security mechanisms, such as rule-based intrusion detection systems and traffic filtering, struggle to effectively counter evolving DDoS tactics due to the increasing complexity and volume of attacks. Artificial Intelligence (AI)-driven approaches provide a more adaptive and intelligent solution by leveraging machine learning (ML) and deep learning (DL) techniques to detect, classify, and mitigate DDoS attacks in real time.AI models analyze vast amounts of network traffic data to identify anomalous patterns, distinguishing between legitimate and malicious requests with high accuracy. Supervised and unsupervised learning techniques, such as Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and neural networks, enhance threat detection by recognizing attack signatures and detecting zero-day attacks. Advanced deep learning architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), further improve the accuracy and speed of anomaly detection by learning from historical attack patterns.In addition to detection, AI-powered mitigation strategies enable dynamic resource allocation, traffic rate limiting, and network traffic redirection. Techniques such as Reinforcement Learning (RL) and Federated Learning (FL) allow adaptive defense mechanisms that continuously evolve based on emerging attack vectors. Software-Defined Networking (SDN) and Network Function Virtualization (NFV) enhance the flexibility of AI-based solutions by enabling real-time monitoring and automated response mechanisms, minimizing downtime and ensuring service availability.This study explores the effectiveness of AI in fortifying cloud networks against DDoS attacks by integrating AI-driven threat intelligence with cloud security frameworks. By automating the detection and response process, AI enhances the scalability, efficiency, and resilience of cloud infrastructures, reducing the impact of DDoS attacks and ensuring uninterrupted service delivery. The integration of AI with cloud security measures represents a transformative approach to mitigating cyber threats in modern digital environments.