Applications of machine learning in nuclear arms control verification

Current nuclear arms control verification methods employ various cryptographic techniques to prevent classified data leakage. Enhancing these methods' accuracy through better data extraction from the cryptographic data is crucial. This study integrated machine learning with physical encryption, training four models for arms control verification. Additionally, we amassed a dataset encompassing most significant fraudulent scenarios via Monte Carlo simulations, representing the inaugural dataset tailored for arms control verification research. Machine learning-based arms control verification technology can automatically extract essential features from encrypted data and make real-time adjustments and optimizations based on new data, significantly enhancing the accuracy. Concurrently, by leveraging physical encryption, classified data is encrypted before electrically recording, reducing tampering and unauthorized access risks. By calculating the models' prediction accuracy, we assessed the performance of each model. The results indicate that the K-Nearest Neighbors, Random Forest, and Support Vector Machine models all exhibited highly accuracy and robust noise resilience. And the Logistic Regression model also demonstrated reliable performance indicators. Furthermore, the exceedingly low probability of successfully decrypting the physical encryption underscores its effectiveness in safeguarding confidential data. In summary, the machine learning-based verification method demonstrates superior accuracy and enhanced efficiency while effectively preventing unauthorized data access or manipulation.