Physical unclonable functions (PUFs) provide a promising solution for enhancing security and device authentication. Strong PUFs can generate quantities of challenge-response pairs(CRPs) but are vulnerable to machine learning (ML) attacks. Weak PUFs must restrict direct access to the original response because they have limited CRPs. In this article, we present a Shuffled Chaotic-dual-PUF structure(SCD-PUF) to defeat against ML attacks. Its working procedure is divided into two main stages: In the first phase, the weak PUF is used to generate secret bits as parameters for the chaotic configuration and along with the secret bits generated by the chaotic process, serve as the obfuscation configuration for the second phase. The second stage involves the Knuth-Durstenfeld shuffle algorithm, concatenation and XOR operations to obfuscate the challenges and responses at the same time. To prove the effectiveness of our proposal, we implement an example of SCD-PUF using Static Random-Access Memory(SRAM) PUF and Arbiter PUF(APUF) on Xilinx ZedBoard FPGAs. Using Logistic Regression (LR), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) as attacking methods, the learning accuracy is maintained at around 51% even when the training data increase to one million, which proves our proposal has enough resistance to ML attacks. Also, the area overhead of our proposal is appropriate and acceptable.