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.

翻译：近年来，神经网络已被用于实现对称密码函数以保障通信安全。为拓展这一领域，本文提出的方法探索了在神经网络框架内应用非对称密码学，以保护两个通信实体（即Alice和Bob）之间的信息交换免受敌对窃听者（即Eve）的攻击。该方法采用一组五个不同的密码密钥，基于椭圆曲线密码学原理，检验通信安全在抵御窃听尝试方面的效能与鲁棒性。实验设置表明，Alice和Bob实现了安全通信，且在不同曲线上安全效能的差异可忽略不计。该方案还旨在评估密码学韧性。具体而言，在加密-解密过程中，Bob的损失指标在0到1之间振荡，表明Alice在加密后能成功理解消息。当解密准确率超过60\%时存在潜在脆弱性，此时Eve接受了增强的对抗训练，其每批训练迭代次数是Alice和Bob的两倍。