Self Healing Cloud IoT Systems Using Adversarial Machine Learning

Abstract

The rapid expansion of Cloud–IoT ecosystems has introduced unprecedented scalability and flexibility but also created complex attack surfaces vulnerable to evolving cyber threats. Traditional intrusion detection and fault-tolerance techniques struggle to address adversarial attacks that exploit machine-learning models and disrupt IoT service continuity. This paper proposes a self-healing Cloud–IoT architecture enhanced by adversarial machine learning (AML) to autonomously detect, mitigate, and recover from malicious disruptions. The framework integrates adversarial-resilient anomaly detection, dynamic attack classification, and automated healing modules that leverage reinforcement learning (RL) and predictive models to restore system functionality with minimal human intervention. Experimental evaluations demonstrate improved robustness, reduced downtime, and higher detection accuracy under various adversarial scenarios, proving the effectiveness of AML-driven self-healing mechanisms for next-generation distributed systems. This work highlights essential design considerations and presents future directions for secure, autonomous, and resilient Cloud–IoT infrastructures.