Neural Network-Based Cryptography Advances with RANDOM Model
/ 4 min read
Quick take - The article discusses the introduction of Neural Network-based cryptography, specifically the RANDOM model, which aims to enhance encryption methods by generating unique, adaptable encryption schemes that resist decryption efforts, while addressing challenges such as communication overhead and bit-recovery errors compared to traditional cryptographic methods.
Fast Facts
- Neural Network (NN)-based cryptography is emerging as a potential alternative to traditional methods like RSA and SHA, offering enhanced resistance to decryption.
- The RANDOM model introduces a framework for Adversarial Neural Cryptography (ANC) with three novel layers to improve computational efficiency and message uniqueness.
- RANDOM achieves 100% accuracy in recovering original messages without quantization, outperforming previous CNN and LSTM networks in both accuracy and uniqueness of encrypted messages.
- The training process for RANDOM emphasizes minimizing the Mean Squared Error (MSE) while maximizing legitimate message recovery against adversarial attempts.
- Testing shows that throughput improves with larger message sizes, with the Apple M1 Pro chip achieving the highest performance among evaluated platforms.
Advancements in Neural Network-Based Cryptography
Advancements in cryptography are taking a significant leap forward with the introduction of Neural Network (NN)-based cryptography. This novel approach is being explored as a potential alternative to traditional cryptographic methods such as RSA and SHA.
Unique Encryption Schemes
NN-based cryptography aims to develop random cryptographic schemes that may offer greater resistance to decryption compared to conventional algorithms designed by humans. A key feature of NN-based cryptography is its ability to generate unique encryption schemes that evolve with each retraining of the neural network. This adaptability presents a considerable challenge for adversaries, who would need to restart their decryption efforts after every retraining session.
Despite its potential, NN-based encryption faces several challenges. These include communication overhead due to error correction, the quantization of continuous outputs, and the implementation of One-Time-Pad (OTP) encryption. To address these issues, the Random Adversarial Data Obfuscation Model (RANDOM) has been introduced as a new framework for Adversarial Neural Cryptography (ANC).
The RANDOM Model
The RANDOM model incorporates three novel neural network layers: a projection layer, an inverse projection layer, and a dot-product layer. These layers collectively aim to enhance computational efficiency while ensuring that encrypted messages remain unique to each encryption key. RANDOM requires approximately 100 Kb of storage and can achieve a throughput of up to 2.5 Mb/s for end-to-end encrypted communication.
The importance of cryptography in maintaining information confidentiality in modern communication systems is significant. Deep learning techniques can address various challenges in communications, such as spectrum management and modulation recognition. However, these techniques can also be exploited by adversaries to compromise information security.
In the ANC framework, both encryption and decryption networks are trained in the presence of an adversary attempting to intercept communications. Previous ANC efforts have utilized Convolutional Neural Networks (CNNs) but encountered issues with bit-recovery errors. The RANDOM model seeks to resolve these errors and operates without the need for output quantization, enhancing the uniqueness of encrypted messages, an essential factor for the security of the ANC network.
Training and Performance
The architecture of RANDOM involves a training process where input data is projected into a higher dimensional space for processing. The encryption network processes the unencrypted message and the encryption key separately before combining them. The decryption network’s role is to recover the original message from the encrypted output. An adversary network, referred to as Eve, attempts to derive the original message from intercepted encrypted communications.
The training of the networks employs a loss function that minimizes the Mean Squared Error (MSE) between the inputs and outputs. The training process emphasizes maximizing the accuracy of message recovery for legitimate users (Bob) while minimizing the adversary’s ability to reconstruct the original message.
A comparative analysis of RANDOM against CNN-based and Long Short-Term Memory (LSTM)-based ANC networks reveals notable differences. RANDOM achieves 100% accuracy in recovering original messages without the need for quantization. In contrast, CNN and LSTM networks display slightly lower accuracy rates with quantization. Furthermore, the average uniqueness of encrypted messages produced by RANDOM reaches a perfect score of 100%, in contrast to the lower percentages achieved by CNN and LSTM networks.
Training parameters for the RANDOM, CNN, and LSTM networks include the use of ADAptive Momentum (ADAM) optimization, with a maximum of 256 epochs used during training. Testing across various computing platforms indicates that throughput improves with increased message sizes, with the Apple M1 Pro chip demonstrating the highest throughput among the evaluated platforms.
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