README.md

metadata

title: Neural Cryptanalysis Lab
emoji: 🔏
colorFrom: purple
colorTo: green
sdk: docker
pinned: true

🔏 Neural Cryptanalysis Lab

Interactive web application for evaluating ML-based neural distinguishers on 14 modern lightweight block ciphers.

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🌐 Web App Features

Run all experiments without touching the command line:

Experiment	Description
① Representation Analysis	Tests all 10 input representations — which format best distinguishes cipher from random?
② Model Comparison	Benchmarks MLP, CNN, SiameseNet, and MINE on the same configuration
③ Round Limits Analysis	Sweeps round counts to find the security boundary of each cipher
④ Confusion Matrix	Visual heatmap of true vs predicted labels for selected ciphers
⑤ Dataset Distribution	Hamming weight distribution of ΔC vs ideal random
⚡ Full Pipeline	Runs all 4 experiments sequentially — mirrors python run_all.py

Bonus Tasks:

• 🔍 Differential Characteristic Search
• ⚖️ Classical Cryptanalysis vs ML Comparison
• 🔀 Transfer Learning across round counts
• 🔑 Partial Key Recovery via distinguisher scoring

Key UI Features

• Multi-select ciphers, models, representations
• Round sweep field (comma-separated, e.g. 3,4,5,6,7)
• Live terminal log during training (SSE streaming)
• Results table with colour-coded accuracy + CSV export
• Pipeline mode shows all 4 plots in a grid on completion

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🛠 Running Locally

git clone https://huggingface.co/spaces/<your-username>/neural-cryptanalysis
cd neural-cryptanalysis
pip install -r requirements.txt
python3 backend_main.py
# Open http://localhost:7860

Or via the original CLI orchestrator:

python3 run_all.py

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🧱 Tech Stack

• Backend: FastAPI + Uvicorn, background threading, SSE streaming
• ML: PyTorch (MLP, CNN, SiameseNet, MINE), scikit-learn
• Frontend: Vanilla HTML/CSS/JS — zero dependencies
• Ciphers (14): SKINNY, CRAFT, ASCON, SATURNIN, GIFT-64/128, XOODOO, GIMLI, SPARKLE, KNOT, QARMA, PIPO, WARP, CHAM
• Representations (10): Raw, Diff, Concat, Bit-Slice, Word, Intermed, Noisy, Joint P-C, Stats, Sequential

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📄 Report

See Neural_Cryptanalysis_Report.pdf for the full academic write-up.