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The integration of machine learning (ML) and artificial intelligence (AI) technologies into the semiconductor industry significantly impacts Platform Security Engineering (PSE):

• Anomaly Detection: Machine learning models, such as those built with XGBoost or neural networks, can be used to detect unusual behavior or anomalies in hardware performance that may indicate a security breach or hardware malfunction. This is critical in maintaining the integrity of semiconductor devices.
• Threat Intelligence: Advanced models like CNNs and Transformers can analyze vast amounts of data to identify potential threats or vulnerabilities more efficiently than traditional methods. They can process and interpret complex patterns within the data, which are indicative of sophisticated cyber threats.
• Automated Security Testing: AI models can automate the process of security testing, making it faster and more thorough. They can simulate a variety of attack scenarios and learn from each testing session, thereby enhancing the ability to predict and mitigate future attacks.
• Biometric Security: In cases where semiconductor devices include biometric security features (like fingerprint sensors or facial recognition technologies), machine learning models are essential for processing and authenticating biometric data with high accuracy and speed.
• Natural Language Processing (NLP): Techniques using BERT and other NLP models can be utilized to analyze and process documentation or communications for secure operations, ensuring compliance with security protocols and detecting sensitive information leaks.
• Enhanced Data Privacy: Machine learning can also be applied to develop and enhance techniques like differential privacy, ensuring that semiconductor devices can handle data in a way that maximizes user privacy.
• Optimization and Efficiency: Models like K-Means can be used for clustering and optimizing various operational parameters in the manufacturing process of semiconductors, indirectly supporting security by ensuring high-quality, defect-free production.