Neural Networks Meet Elliptic Curve Cryptography: A Novel Approach to Secure Communication

TL;DR

This paper introduces a neural network-based approach integrating elliptic curve cryptography for secure asymmetric communication, demonstrating robustness and resilience against eavesdropping in experimental setups.

Contribution

It presents a novel neural network framework applying elliptic curve cryptography for asymmetric secure communication, extending neural cryptography beyond symmetric functions.

Findings

Secure communication achieved with negligible security variation across different elliptic curves.

Loss metrics indicate successful message decryption by Alice and Bob.

Eavesdropper vulnerability increases with adversarial training, but overall security remains robust.

Abstract

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.