Quantum-Powered Extended Visibility for Zero-Trust-Based Ransomware Detection in Smart Grids

Technological evolution in the Industrial Internet of Things (IIoT) domain has fostered smart grid systems’ operation, performance, connectivity, and delivery with higher efficiency. However, it has also exposed the platform to a broader surface for attackers. Current information technology (IT)-centric solutions for detecting, preventing, and mitigating attacks have limitations, especially in comprehensively monitoring industrial control operational technology (OT) and communication systems. The rise of sophisticated cyberattacks, such as targeted ransomware, demand more robust security measures, leading to the emergence of zero trust (ZT) deployment as a response to these threats. This article proposes a new framework for implementing ZT comprising both IT and OT in smart grid infrastructures, with multiple security mechanisms and robust system coverage. We present an EigenGame algorithm for integrating diverse data sources into a rich-context format and an enhanced approach to quantum reinforcement learning for reliable malicious behavior detection in IIoT-enabled smart grids. The framework was evaluated using five sets of data from the X-IIoTID dataset, demonstrating its good performance in verifying any behavior inside the system and identifying any malicious behavior related ransomware attacks.