Detection of DDOS Attacks and Classification/strong>
Authors:-Gopi A G, Professor Dr. M Anand Kumar
Abstract-Distributed Denial of Service (DDoS) attacks are a significant threat to the stability and availability of network services, often resulting in financial and reputational damage to organizations. Detecting and mitigating these attacks is a complex task due to their large scale, diverse attack vectors, and evolving nature. This paper explores various methods for DDoS attack detection and classification, with a focus on leveraging machine learning and statistical techniques. The primary objective is to identify attack patterns in network traffic data and classify them in real-time to distinguish between legitimate and malicious activities. We review traditional methods such as signature-based detection and anomaly detection, alongside modern machine learning-based approaches, including supervised and unsupervised classification techniques. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are evaluated for their effectiveness in detecting various types of DDoS attacks, including volumetric, protocol, and application-layer attacks. Additionally, we discuss the challenges posed by high traffic volumes, the need for low-latency detection, and the impact of adversarial tactics on detection systems. Finally, the paper highlights the importance of developing robust, scalable, and adaptive classification models that can efficiently handle the evolving nature of DDoS attacks in dynamic network environments.
