Cybersecurity Monitoring of Quantum Cyber-Physical Systems Using Artificial Intelligence: Detection of False Data Injection Cyber-Attacks in Photonics-Driven Quantum Information

Quantum cyber-physical systems can involve computation-based strategies like quantum algorithms, physical parts, and communication-based infrastructures such as physical quantum bits (qubits), measurement units, and communication links. A photonics-driven quantum cyber-physical system can rely on physical parameters of the environment or communication links such as the refractive index. The injection of false data into the actual value of the refractive index can cause the use of an incorrect value of the refractive index in the system if needed, and as a result, an incorrect interpretation of information or even access to non-real information instead of the actual information. The first attempt to avoid this issue can be the detection of the existence of the cyber-attacks in the system. This paper will address this challenge using artificial intelligence for binary classification to detect the cyber-attacks in the system. For the proof of concept, the proposed strategy is examined deploying the real and imaginary parts of the refractive index value corresponding to a semiconductor, i.e., GaAs. Different scenarios are performed, including a single evaluation, an analysis of several training runs, a comparison considering two types of normalization techniques, variations in the size of artificial intelligence, and a comparison among different machine learning techniques, including artificial neural networks, decision tree models, a logistic regression model, and support vector machine classifiers. Based on the obtained results, for the single evaluation, the class of 95.24 % of the testing samples could be classified successfully. In addition, for the case of the analysis of several training runs, a total of 9000 runs were run for 60 shallow artificial neural networks with different sizes. For 58 neural networks, the maximum achieved accuracy was 100 %. Besides, for the case of the comparison between the normalization techniques, two methods were evaluated, i.e., min-max and z-score normalization. The results were very close to each other, but, more accurately, z-score normalization indicated a better performance and a higher accuracy. Finally, among the mentioned machine learning models, artificial neural networks mostly showed higher accuracies.