Repurposing Neural Networks for Efficient Cryptographic Computation

—While neural networks (NNs) are traditionally associated with tasks such as image recognition and natural language processing, this paper presents a novel application of NNs for efficient cryptographic computations. Leveraging the Turing completeness and inherent adaptability of NN models, we propose a transformative approach that efficiently accelerates cryptographic computations on various platforms. More specifically, with a program translation framework that converts traditional cryptographic algorithms into NN models, our proof-of-concept implementations in TensorFlow demonstrate substantial performance improvements: encryption speeds for AES, Chacha20, and Salsa20 show increases of up to 4.09 × , 5.44 × , and 5.06 × , respectively, compared to existing GPU-based cryptographic solutions written by human experts. These enhancements are achieved without compromising the security of the original cryptographic algorithms, ensuring that our neural network-based approach maintains robust security standards. This repurposing of NNs opens new pathways for the development of scalable, efficient, and secure cryptographic systems that can adapt to the evolving demands of modern computing environments.