Deep Learning Algorithms for Detecting and Mitigating DDoS Attacks

Abstract

Raising the threat of Distributed Denial of Service (DDoS) attacks means that high and adapted detection tools are required now more than ever. This research focuses on exploring the latest solutions in preventing DDoS attacks and emphasizes how Artificial Intelligence (AI) is involved in enhancing end-to-end detection techniques. Through the analysis of several key approaches, this work notes that AI-guided models quickly identify and counteract any unusual traffic patterns that may indicate an oncoming DDoS attack. Essential aspects towards creating more resilient networks against such attacks include machine learning algorithms, sophisticated data analytics together with AI based detection systems for traffic pattern recognition. Importantly, AI does well in behavioral analysis because it can distinguish and adapt to changing attack vectors. Additionally, it puts AI into perspective as making positive mitigation strategies possible that contain quick interferences such as temporary halt of traffic, rerouting and targeted block listing with real time control panel operations. On the contrary, current DDoS detection prevention techniques remain critically addressed of persistent challenges and limitations fundamental to them. From what emerges, they should always be ready for innovation and improvement because of how attacks might evolve over time. This paper aligns itself with the position that AI-driven detection mechanisms are natural to network security against DDoS attacks. It underlines the importance of integrating AI-based solutions with conventional practices in order to enhance network resilience and efficiently counteract cyber threats that are evolving all the time.