  In recent years, neural networks have been used to implement symmetric
cryptographic functions for secure communications. Extending this domain, the
proposed approach explores the application of asymmetric cryptography within a
neural network framework to safeguard the exchange between two communicating
entities, i.e., Alice and Bob, from an adversarial eavesdropper, i.e., Eve. It
employs a set of five distinct cryptographic keys to examine the efficacy and
robustness of communication security against eavesdropping attempts using the
principles of elliptic curve cryptography. The experimental setup reveals that
Alice and Bob achieve secure communication with negligible variation in
security effectiveness across different curves. It is also designed to evaluate
cryptographic resilience. Specifically, the loss metrics for Bob oscillate
between 0 and 1 during encryption-decryption processes, indicating successful
message comprehension post-encryption by Alice. The potential vulnerability
with a decryption accuracy exceeds 60\%, where Eve experiences enhanced
adversarial training, receiving twice the training iterations per batch
compared to Alice and Bob.
