Integrating Data Science and Cybersecurity Strategies for Enhancing Power System Resilience

The following paper aims at analyzing the role of data science and cybersecurity in strengthening the power systems. Globally, the threats are becoming more complex and frequent where conventional security measures fail to provide the much-needed security solutions. The current paper provides the comparative analysis of data science solutions to enhance the threat detection and response, such as machine learning algorithms and real-time anomaly detection systems. Neural Networks were identified to have yielded the highest detection rates and the least false positive returns and On the other hand clustering based anomaly detection systems was identified as most efficient when used in real time scenarios. The use of such methods along with conventional approaches in cybersecurity enhances the capacities for detecting and mitigating emerging threats hence strengthening the existing power systems. The findings therefore support the development of complex methodologies that enhance the fight against new and changing threats in the protection if critical infrastructure.