Published Paper Details:

Distributed Denial-of-Service (DDoS) attacks have emerged as a significant cybersecurity threat, disrupting online services and causing financial and reputational damage to organizations worldwide. This paper presents a conceptual framework for understanding DDoS attacks by classifying them based on attack vectors, impact and methodology. It explores analytical models, including game theory, queuing theory and entropy-based approaches, to understand the attack-defense dynamics and assess the effectiveness of various mitigation strategies. Furthermore, this paper analyzes traditional and modern defense mechanisms, including rate limiting, anomaly detection, machine learning-based classifiers and blockchain-based mitigation. By examining the evolution of DDoS attacks and countermeasures, this paper provides insights into future challenges and research directions in securing networks against increasingly sophisticated attack strategies. The findings highlight the necessity of a multi-layered defense approach that integrates theoretical models with practical implementations to enhance resilience against DDoS threats.