Cryptography is a fundamental cornerstone of security on the modern internet, facilitating the confidentiality, integrity and authenticity of data. Most cryptography in use today base their security on the assumed hardness on certain computational problems like the discrete logarithm problem or prime factorization of large integers. Both of these are problems that are easy to solve for a quantum computer, which motivates the need to transition to post quantum encryption (PQC). Several PQC candidate encryption methods have been suggested, with NIST standardizing algorithms for key exchange and digital signatures. There are also methods such as hybrid encryption where a PQC algorithm is combined with a classical (non-PQC) algorithm, to create a hybrid scheme. Utilizing hybrid encryption is an essential technique to accelerate the transition to Post quantum cryptography. Preliminary studies have shown that neural network can identify weak encryption algorithms, and our goal is to expand this analysis to identifying potential weaknesses introduced when combining encryption algorithms in hybrid encryption. This is a study to investigate how cryptographic security can be verified using neural networks, by training neural networks to distinguish between different classes of ciphertext that by assumption should be indistinguishable.