PUF Integrated Circuit with Dynamic Conversion for ML Attack Resistance

The patent applies dynamics by making the internal connection structure of the conversion unit changeable rather than fixed. The conversion unit can dynamically reconfigure its internal connections based on challenge signals, creating different transformation paths for each challenge. This dynamic adaptability prevents machine learning models from predicting outputs, as the system's behavior changes with each interaction, thereby improving security detection capability without requiring additional operational processing overhead.

The patent changes the structural parameters of the conversion unit by introducing changeable internal connection structures. Instead of using a fixed circuit topology, the conversion unit's internal connections are modified based on challenge inputs. This parameter change approach transforms the PUF system from a static to a dynamic system, making it resistant to machine learning attacks while maintaining efficient operation without additional overhead.

The conversion unit employs a dynamic internal connection structure that changes based on challenge signals. This dynamic configuration allows the system to adapt its transformation paths for each challenge, improving security performance by preventing pattern recognition by machine learning models. The dynamic nature of the connections provides security enhancement without requiring complex additional external processing components.

The conversion unit is designed to perform multiple functions through its changeable internal connection structure. The same conversion unit handles different transformation tasks for each challenge by reconfiguring its internal connections, eliminating the need for separate dedicated circuits for each function. This multi-functionality approach improves security performance while avoiding the complexity increase that would result from adding separate specialized components.