AN EVALUATION OF MITIGATION STRATEGIES FOR DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACKS IN ENTERPRISE NETWORKS
Abstract
Distributed Denial of Service (DDoS) attacks pose a significant threat to enterprise networks by overwhelming systems and disrupting services. This study evaluates various mitigation strategies designed to detect, prevent, and respond to DDoS attacks in organizational environments. It examines traditional approaches such as firewalls, intrusion detection systems (IDS), and rate limiting, alongside advanced techniques including machine learning-based detection, cloud-based mitigation services, and traffic filtering mechanisms. The research analyzes these strategies in terms of effectiveness, response time, scalability, and cost efficiency. Results indicate that while traditional methods provide baseline protection, modern DDoS attacks require more adaptive and intelligent solutions. Hybrid approaches that combine real-time monitoring with automated response systems prove to be the most effective in minimizing downtime and maintaining network performance. The study emphasizes the need for continuous innovation and layered defense mechanisms to strengthen enterprise network resilience against evolving cyber threats.
