Hybrid LSTM-QEQ Framework for Enhanced Cyber Threat Detection and Response in Critical Power Grid Infrastructure

The rising multifaceted nature of cyber threats to critical Power Grid Infrastructure calls for new approaches in ensuring power systems resiliency and stability. Existing methods based on deep learning and predictive analysis have shown significant gains in the area of threat mitigation and response time. However, such approaches have been rendered limited in their scope by their lack of dynamic reaction to the shifts in cyberattack tactics, particularly in sequential and multi-dimensional settings. This paper presents the novel Power Grid Security Alert and Emergency Response System by integrating Long Short-Term Memory (LSTM) networks into a Quantum Entropy Q-Learning (QEQ) model. The hybrid LSTM-QEQ framework utilizes the complementarity between the temporal structure modeling abilities of the LSTMs and decision-making based on entropy optimization by QEQ in detecting, predicting, and responding to cyber threats more efficiently. The developed system overcomes some of the shortcomings of existing methods by improving resilience to complex attack scenarios and guaranteeing optimal responses through quantum-inspired learning. This solution enables real-time detection of anomalies, pre-emptive failure prevention, and appropriate responses to a scenario. The main benefits include higher threat detection rates (TDR), lower response time to incidents, and the ability to operate in large and flexible grid networks. With the application of artificial intelligence (AI) and quantum concepts, this work provides a solution to further enhance the performance of critical energy assets while strengthening the security against contemporary cyber threats. The results suggest that there has been a notable improvement in the TDR, now standing at 99%, and a reduction in incident response time of over 75%, witnessing the potential AI holds for efficient and timely identification, as well as the mitigation of cyber-related risks. Moreover, deep learning algorithms have made it possible to forecast failures and cyber-attacks, enhancing proactivity and cyber security management greatly with regard to Power Grid Infrastructure.