DDoS Protection using Machine Learning: A Modern Approach to Cybersecurity
Author(s):
Gnana Teja Reddy Nelavoy Rajendra
Distributed Denial of Service (DDoS) attacks are rapidly evolving and present new cybersecurity threats that need to be addressed through better measures. Machine learning (ML) is becoming a key solution in improving DDoS solutions since it provides adaptability and predictive safeguards against these complex threats. This paper discusses how ML has adapted and enhanced the approaches to DDoS mitigation, explaining how anomaly detection, adaptive filtering, and real-time decision-making contribute to strengthening the DDoS defense mechanisms. The use of ML in developing autonomous cybersecurity systems is pointed out as a significant achievement because of its capability to administer responses in real-time, independently. The necessity of cooperating in the industry to share data is analyzed since consolida2ting these efforts increases the chances of identifying new threats using ML models. The recent development in deep learning, which enables the correct identification of intricate attack patterns, and applying blockchain technology with ML, which enhances decentralized security systems, are also highlighted. Such advancements suggest a future where AI-connected cybersecurity systems will offer self-driven, reinforced protection capable of evolving to meet the current trends of the constant rise in cases of cyber-crimes. With the increased complexity of cyber threats, companies must embrace ML-based solutions to outcompete cybercriminals, minimize disruptions, and protect critical assets. This paper, therefore, stresses the need to continue with research and development to enhance the application of ML in DDoS mitigation to guarantee proactive, adaptive, and robust protection of the networks against future onslaughts.
