SNN-Comprypto: High-Performance Compression and Encryption Using Spiking Neural Network Chaotic Reservoir Dynamics

I propose SNN-Comprypto, a novel system that leverages the chaotic dynamics of Spiking Neural Networks (SNNs) to perform simultaneous high-performance data compression and encryption. Unlike conventional methods that treat compression and encryption as separate processes, this approach integrates both within a single reservoir computing architecture. **Version History:** - **v1**: Core system with predictive compression and chaotic encryption. Passes all 9 NIST SP 800-22 randomness tests. Achieves 100% lossless reconstruction and strong avalanche effect (0.70% match rate with 1-bit key change). - **v2**: Introduces temperature parameter as a second cryptographic key (0.0001 difference causes complete decryption failure). Presents phase transition analysis identifying optimal neuron counts (critical point at 100 neurons, sweet spot at 240 neurons). - **v3**: Adversarial evaluation framework demonstrating SNN superiority for random number generation. Results from 100,000+ rounds show SNN achieves 0.39% prediction rate (matching theoretical random), while DNN is 18× and LSTM is 55× more predictable. - **v4**: Theoretical chaos analysis with Lyapunov exponents and entropy measurements. SNN achieves near-perfect entropy (7.998/8.0 bits) with positive Lyapunov exponents, confirming true chaotic dynamics. Adversarial attack resistance evaluation (4/5 attack types resisted). IV/nonce recommendation for secure deployment added. - **v5 (NEW)**: Adaptive Compression Engine achieving compression ratios as low as 2.9% for binary data, outperforming standard zlib (which expands to 104.3%). Automatic selection of optimal encoding method (Raw/Delta/XOR). Verified with text, binary, and image files with perfect lossless reconstruction. Source code: https://github.com/hafufu-stack/temporal-coding-simulation