This executable blog post is the fourth in a series related to machine learning and is a fascinating trifecta involving hardened cryptography software, embedded IoT-type hardware, and deep machine learning techniques. While the AES algorithm is designed such that a brute-force secret key guessing attack would likely finish ‘sometime near eternity’, the power side-channel attack demonstrated here retrieves the 128-bit secret key ‘probably closer to lunchtime’. After reviewing the specific attack scenario with its hardware and software elements, we utilize publicly available benchmark profiling data to train a deep machine learning model to support secret key extraction. We then proceed through a methodical process that begins with intermediate model predictions from benchmark attack data and removes the hardening protections to ultimately produce a secret key from approximately 40-100 power traces taken together. While the benchmark-oriented scenario is simplified for clarity, it is very indicative of the difficulty of protecting cryptographic primitives running on embedded hardware from power side-channel attacks.
The Jupyter-based notebook can be found here
