Quantum-safe cyber digital twin architectures for proactive threat forecasting and adaptive defense

Abstract As the recent developments in digital infrastructures have led to increased risk of cyber threats. Among them, quantum technology assisted threats signal an unprecedented and urgent threat type referring to cyberattacks that utilize quantum computing and quantum mechanics to attack traditional cryptographic solutions. For example, algorithms such as Shor’s offer the capability to breach RSA and ECC encryption exponentially faster than any classical-based route resulting in the potential for unauthorized access to vast amounts of data and destabilization of sensitive systems over a longer time period. More differently than traditional attacks, quantum empowered threats are uniquely dangerous in that, as quantum hardware becomes sufficiently capable or complex, they can depend on captured data to retroactively decrypt messages. Current security mechanisms that are reactive-based do not anticipate nor defend against these threats, leaving critical networks vulnerable to threats faced by both classical actors and actors capable of quantum-enabled computation. To tackle this massive and dangerous challenge, we propose a new Quantum-Safe Cyber Digital Twin (QS-CDT) architecture that is intended to purposely forecast cyber threats and dynamically orchestrate adaptive defences. QS-CDT maintains a dynamic digital twin of the live network that is always up-to-date and is protected using post-quantum algorithms that are both CRYSTALS-Kyber and Dilithium for end-to-end quantum resilience. State-of-the-art quantum inspired machine learning algorithms in the twin will simulate and consider attack vectors and emerging threats in real-time, thus enabling preventive action through the knowledge of the threats, their impacts and potential mitigation actions. Here, quantum-inspired machine learning refers to classical algorithms executed on conventional hardware that adopt mathematical principles from quantum computing, such as tensor networks and variational optimization, without requiring physical quantum processors. Unlike other digital twin approaches which center on replicating and passively monitoring the system, the proposed approach combines real-time simulation of potential threats with an automated, policy-driven reconfiguration of the network. Our initial simulation trials show a 35% improvement in latency of threat detection and a 40% improvement in adaptive response time to accommodate unpredictable responses than a less efficient, traditional reactive approach. This improves in turn the overall security posture of the network. Future work shall focus on expanding QS-CDT to large, heterogeneous networks, improve the predictive modeling of quantum potential threat scenarios, and assimilate the architecture with existing operational cyber defense networks. These findings represent an important platform for a next-generation proactive cybersecurity model in the post-quantum computing era.