🌟🧠 SCAAML by Google: Deep Learning Framework for Side-Channel Attacks (Python Practice)

While many DL tools for SCA focus on generic classification, SCAAML (Side Channel Attacks Assisted with Machine Learning) from Google delivers a dedicated TensorFlow 2.x framework built specifically for real side-channel attacks. It emphasizes practical key recovery on cryptographic implementations like AES, with clean Python APIs for dataset handling, model training, and attack evaluation.

🧠 Core Concept
- Purpose-Built for SCA: Ready-to-use pipelines for profiling attacks, including tailored CNN architectures optimized for power/EM leakage.
- Key Rank & Guessing Entropy Focus: Built-in support for SCA-specific evaluation metrics that directly measure attack success (how quickly the model recovers the secret key).
- End-to-End Workflow: From trace loading and preprocessing to training and full attack simulation — all in Python, easy to run on your hardware (CPU/GPU/Jetson).

💻 Implementation Example

from scaaml import Dataset, Model, Attack

# Load or create dataset (supports common SCA formats)
dataset = Dataset.from_h5("path/to/your_traces.h5")  # or use built-in loaders

# Define and train model (example CNN tuned for TinyAES)
model = Model.cnn_architecture(input_shape=dataset.shape, 
                               num_classes=256)  # HW or ID leakage

model.compile(optimizer='adam', loss='categorical_crossentropy', 
              metrics=['accuracy'])

model.fit(dataset.train_traces, dataset.train_labels, 
          epochs=50, batch_size=256, validation_split=0.1)

# Perform the attack and evaluate key recovery
attack = Attack(model=model, dataset=dataset)
key_rank = attack.perform_attack_on_test_set(
    num_traces=1000,  # vary for guessing entropy curves
    byte_position=0
)

print("Best key rank:", key_rank)
# Visualize guessing entropy / success rate
attack.plot_guessing_entropy()

🔥 Use Cases
- Practical AES Attacks: Train models on ASCAD, DPAcontest, or your ChipWhisperer traces and measure real key recovery performance.
- Rapid Experimentation: Quickly iterate on architectures, leakage models (HW/ID), and preprocessing — perfect for benchmarking on Jetson Orin or RTX setups.

⚠️ Caveats & Responsible Practice
- Scope & Limitations: Currently strongest on unprotected or lightly protected implementations (TinyAES etc.). Real-world masked or desynchronized targets require additional techniques and more traces.
- Resource Usage: Training on large trace sets is GPU-friendly but memory-intensive — monitor VRAM on laptops/Jetson and use gradient accumulation if needed.
- Reproducibility: Always use fixed seeds and document preprocessing; results can vary significantly across datasets and random initializations.
- Ethical Boundaries: SCAAML and similar tools are for authorized security research, vulnerability assessment, and educational purposes only. Never use on systems you do not own or have explicit permission to test. Comply with all applicable laws.

🔗 Resources
- SCAAML GitHub

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