Adaptive Threat Response in Edge-Based IoT Systems Using Reinforcement Learning

The rapid growth of Internet of Things (IoT) deployments at the network edge has opened new avenues for real-time data processing and decision-making. However, this proliferation also introduces a broader attack surface, increasing the risk of dynamic and sophisticated cyber threats. Traditional security solutions, often centralized and static in nature, struggle to keep pace with the fluid and decentralized nature of edge-based IoT environments. In this work, we present an adaptive threat response framework that leverages reinforcement learning (RL) to detect and respond to anomalies in real time. By deploying lightweight RL agents at the edge, our system continuously learns from local observations and dynamically adjusts its defensive strategies to counter evolving threats. The framework is designed to be scalable, resource-efficient, and capable of operating with limited connectivity to centralized infrastructure. We evaluate the model across several simulated attack scenarios, demonstrating its ability to improve detection accuracy, reduce response latency, and minimize false positives compared to conventional rule-based systems. This research offers a promising path forward for enabling intelligent, self-adaptive security in distributed IoT ecosystems.