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The Bistable Ring (BR) Physical Unclonable Function (PUF) is a newly proposed hardware security primitive in the PUF family. In this work, we comprehensively evaluate its resilience against Machine Learning (ML) modeling attacks. Based on the success of ML attacks, we propose XOR strategies to enhance the security of BR PUFs. Our results show that the XOR BR PUF with more than four parallel BR PUFs is able to resist the ML modeling methods in this work. We also evaluate the other PUF metrics of reliability, uniqueness and uniformity, and find that the XOR function is also effective in improving the uniformity of BR PUFs.