In this paper, we explored the application of self-supervised learning for CAD software anomaly detection. Our approach leverages self-supervised learning to identify anomalies in CAD software usage patterns, enabling early detection and mitigation of potential issues. Our results demonstrate the effectiveness of our approach, and we believe that it has the potential to improve the reliability and security of CAD software.
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