Abstract: The increasing adoption of Internet of Things (IoT) devices in Cameroon presents significant security challenges, particularly concerning regulatory compliance. Ensuring secure and adaptive management of IoT systems is critical to mitigating cyber risks while aligning with national regulations.

This study investigates the use of Reinforcement Learning (RL) for enhancing IoT security management in Cameroon, with a particular focus on compliance with national cybersecurity regulations (e.g., Law No. 2010/012). Using a Markov Decision Process (MDP), the research defines regulatory-compliant state and action spaces, and trains a Q-learning agent within a simulated IoT environment (CyberBattleSim).

Internet of Things, Reinforcement Learning, Regulatory Compliance, Markov Decision Process, Cybersecurity, Cameroon, Q-learning, Intrusion Detection, Adaptive Security Management

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