A Comprehensive Cybersecurity Resilience Framework Augmenting Smart Grid Stability

Smart grids have emerged as intelligent systems comprising efficient sensor technology for effective power transmission and distribution. These critical modern infrastructures for energy distribution have been facing escalating cybersecurity threats. This research presents a novel cybersecurity framework for the mitigation and prevention of such attacks. The framework proposed involves a machine learning based defense mechanism that assists in enhancing the grid’s reliability even further against potential attacks. The research uses a computer simulated setting to evaluate cybersecurity attack scenarios and their effects, on grid operations and overall system security. This article provides a review of existing literature on the cybersecurity challenges that modern smart grids encounter along with the strategies used to counter these threats. The developed framework serves as a cybersecurity solution that employs mathematical methods to bolster grid stability and management identify anomalies and prevent potential cyber-attacks. The framework also leverages Machine Learning models to refine its operations and enhance its ability to identify risks effectively. The results indicate that this innovative approach surpasses methods in reducing cybersecurity risks of this nature. The effectiveness of the proposed cybersecurity model, in mitigating cyber threats within grids is further validated by its detection accuracy rate.