Neural Networks Meet Elliptic Curve Cryptography: A Novel Approach to Secure Communication
This addresses secure communication for entities like Alice and Bob against eavesdroppers, but it appears incremental as it extends neural networks to asymmetric cryptography without major breakthroughs.
The paper tackles secure communication by applying neural networks to asymmetric cryptography using elliptic curve principles, achieving secure exchange with negligible variation across curves and showing Bob's loss metrics between 0 and 1 indicating successful decryption.
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.