Reinforcement learning-based intrusion prevention in M2M systems

Abstract

The increasing reliance on Machine-to-Machine (M2M) communication in smart environments has led to significant improvements in automation, real-time data exchange, and decision-making. However, the rapid proliferation of connected devices also broadens the attack surface, making M2M systems vulnerable to sophisticated cyber threats. Traditional intrusion prevention techniques often struggle to adapt to the dynamic and distributed nature of these networks. In this study, we propose a reinforcement learning-based intrusion prevention framework tailored for M2M systems. By enabling autonomous agents to learn optimal defense strategies through continuous interaction with the environment, the system can proactively detect and mitigate malicious activities in real time. The model adapts to evolving threat patterns without relying on predefined signatures, making it particularly effective against zero-day attacks. Experimental results demonstrate that the proposed approach significantly improves detection accuracy, reduces false positives, and enhances the overall resilience of M2M communications. This work highlights the potential of intelligent, self-learning systems to secure future machine-driven ecosystems.