Method for improving ability of strong puf to resist machine learning attacks

A method for improving an ability of a strong PUF (physically unclonable function) to resist machine learning attacks, comprising:

• when a strong PUF needs to generate a PUF response sequence, before generating a PUF response for the PUF response sequence each time, obfuscating a current challenge of the strong PUF to generate a cryptographic challenge, so that the strong PUF generates the PUF response under an action of cryptographic challenge, wherein each cryptographic challenge is generated in following way:
• firstly, performing conversion on a Rubik's cube matrix on a basis of the current challenge to generate a cryptographic matrix, and constructing a challenge matrix based on the current challenge;
• then, determining a matrix multiplication pattern of the cryptographic matrix and the challenge matrix on the basis of the cryptographic matrix, multiplying the cryptographic matrix by the challenge matrix to obtain an obfuscation matrix, converting elements in the obfuscation matrix to 0 or 1 according to a parity of the elements to obtain a cryptographic challenge matrix;
• and finally extracting the elements in the cryptographic challenge matrix to form the cryptographic challenge, wherein if the PUF response generated currently is the first PUF response of the PUF response sequence, the Rubik's cube matrix that generates the cryptographic challenge in the current obfuscation is randomly generated;
• if the PUF response generated currently is not the first PUF response of the PUF response sequence, the Rubik's cube matrix that generates the cryptographic challenge in the current obfuscation is a cryptographic matrix generated by a previous obfuscation.