Smart Grids Intrusion Detection Using Cybersecurity Resilience for Reinforcement Deep Learning

Smart grids are modernized power networks that integrate digital communication and control systems, making them vulnerable to cyber threats. Ensuring cybersecurity resilience is essential to maintain reliable and secure energy distribution. Existing intrusion detection methods often suffer from high false alarm rates, limited adaptability, and inefficiency in identifying sophisticated cyberattacks. To address these challenges, this study proposes a Reinforcement Learning-Based Adaptive Intrusion Detection System (RL-AIDS), which employs deep reinforcement learning to dynamically adapt to evolving cyber threats. RL-AIDS enhances detection accuracy by leveraging real-time anomaly analysis and intelligent decision-making. The proposed method strengthens cybersecurity by improving intrusion response efficiency and reducing false positives in smart grid networks. Experimental results demonstrate that RL-AIDS outperforms conventional approaches in detecting cyber intrusions with higher precision and adaptability. The findings indicate that RL-AIDS significantly enhances smart grid security, ensuring a more resilient and reliable power infrastructure against evolving cyber threats.