Real-Time DDoS Attack Detection and Prevention Using Hybrid Machine Learning Techniques

In recent years, the frequency, scale, and sophistication of Distributed Denial of Service (DDoS) attacks have grown exponentially, posing severe threats to organizations, critical infrastructures, and online services. These attacks aim to disrupt the normal functioning of a network or service by overwhelming it with a flood of malicious traffic, rendering legitimate access impossible. With the rapid expansion of internet-connected systems and the increasing reliance on cloud and edge computing, the ability to detect and mitigate DDoS attacks in real-time has become an essential requirement for maintaining cybersecurity and service availability. This research paper proposes a real-time DDoS attack detection and prevention model that utilizes a hybrid approach combining machine learning algorithms with network behavior analysis. The system is designed to monitor traffic patterns continuously and classify incoming traffic as either legitimate or malicious based on extracted features such as traffic flow rate, packet size, connection frequency, and source entropy. A layered architecture is introduced, where a lightweight intrusion detection module performs real-time packet analysis, and a secondary machine learning layer refines detection accuracy by identifying hidden patterns and anomalies that may bypass traditional rule-based systems. To train and evaluate the model, publicly available datasets such as CICIDS2017 and CAIDA are used, providing a comprehensive representation of both benign and attack traffic. The research implements classification algorithms including Random Forest, Support Vector Machine (SVM), and Gradient Boosting to determine the most effective technique in terms of accuracy, false positive rate, and computational efficiency. Experimental results show that the proposed model achieves high detection accuracy (above 97%) with minimal latency, making it suitable for deployment in enterprise and cloud environments. Additionally, the system integrates automatic mitigation techniques such as IP blacklisting, traffic throttling, and adaptive filtering to ensure that malicious traffic is blocked in real-time without affecting legitimate users. By combining fast detection with proactive prevention, the proposed framework ensures minimal service disruption and enhances overall resilience against DDoS threats. The significance of this research lies in its ability to offer a scalable, adaptive, and low-overhead solution for real-time DDoS attack defense. Unlike conventional reactive systems, this model emphasizes proactive threat management, enabling organizations to respond to evolving attack vectors effectively. Future improvements may include integrating deep learning for enhanced anomaly detection, leveraging Software-Defined Networking (SDN) for dynamic traffic rerouting, and expanding the system's capability to detect multi-vector attacks. This paper contributes to the growing body of research focused on fortifying network infrastructures against real-time cyber threats and serves as a foundational model for building more intelligent, automated, and responsive cybersecurity solutions.