A Feedback-Controlled Optimization Approach to Minimize Ransomware Propagation in Internet of Things Networks

The proliferation of Internet of Things (IoT) devices has expanded the attack surface for ransomware, creating new challenges in securing highly distributed and resource-constrained networks. A novel feedback-controlled optimization approach addresses this challenge by dynamically adjusting security measures, including patching frequency, device isolation, and network segmentation, based on real-time infection metrics. The system's ability to continuously monitor network conditions and optimize control parameters provides a significant advantage over traditional static defenses, particularly in mitigating rapidly evolving ransomware threats. Simulations conducted across various IoT network topologies demonstrate the approach's effectiveness in reducing infection rates, minimizing containment times, and efficiently utilizing network resources. The proposed methodology offers a scalable, adaptive solution that can be applied to a wide range of IoT environments, ensuring robust protection against both known and emerging ransomware attacks.