ADVERSARIALLY ROBUST MULTI-STAGE DDOS DETECTION SYSTEM FOR IOT NETWORKS

Abstract

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the attack surface for cyber threats, particularly Distributed Denial of Service (DDoS) attacks, which can severely disrupt network availability and degrade system performance. Traditional intrusion detection systems often struggle to identify sophisticated and evolving DDoS attack patterns in IoT environments due to resource constraints, heterogeneous device behavior, and high traffic variability. This paper proposes a multi-stage adversarial-defense framework for online DDoS attack detection in IoT systems, leveraging advanced machine learning and deep learning techniques to enhance detection accuracy and robustness. The framework operates in multiple stages, including traffic preprocessing, feature extraction, anomaly detection, and adversarial defense mechanisms designed to withstand evasion attacks. By incorporating adversarial training and ensemble learning strategies, the system improves resilience against malicious attempts to bypass detection models. Real-time data analysis enables the framework to detect both known and zero-day attacks with high precision while minimizing false positives. Experimental evaluation demonstrates that the proposed approach outperforms traditional detection methods in terms of accuracy, detection rate, and response time. Furthermore, the system is optimized for resource-constrained IoT environments, ensuring scalability and efficiency. The results highlight the effectiveness of integrating adversarial defense mechanisms with multi-stage detection architectures to secure IoT networks against evolving DDoS threats. This research contributes to the development of intelligent and robust cybersecurity solutions for next-generation IoT infrastructures.