Hacker's guide to deep-learning side-channel attacks: the theory
This blog post series provides a practical step-by-step guide to using deep learning to carry out a side-channel attack -- one of the most powerful cryptanalysis techniques. We are going to teach you how to use TensorFlow to recover the AES key used by the TinyAES implementation running on an ARM CPU (STM32F415) from power consumption traces.
Hardware-backed encryption is the cornerstone of system security. From operating systems’ trusted boots, to phone data encryption, to fingerprint storage, to credit card chip, to Bitcoin hardware wallets used for cryptocurrency storage (e.g Bitcoin), hardware-backed encryption is used everywhere to secure secrets.
Over the last three years, our research team at Google in partnership with various research groups has been working on developing deep-learning side-channels attacks (SCAAML) and countermeasures to help build more secure trusted hardware. In our experience, everything becomes much clearer when you start writing code. That’s why we thought that the best way to help you get started in the exciting field of hardware cryptanalysis would be to provide you with a practical guide that shows you, step by step, how you can recover AES keys from a real hardware target by training a TensorFlow model on CPU power traces.
Lightweight theory: The first part (this post) is dedicated to explaining the core concepts you need to know to understand how a deep-learning power based side-channel attack works, what hardware & software setup you need to carry it out, and a short explanation of the advantage of using deep learning to perform a SCA (side channel attack) over traditional methods such as template attacks. It's a fairly long post (>5800 words), but there are a lot of concepts you need to bring together to get such attacks to work and understanding how everything fits together is one of the main difficulties to get started in hardware cryptanalysis. This is why I did my best to strike the right balance between keeping it brief and providing you with enough explanation and examples so it's clear how a side-channel attack works. The depth and length also explains why it took me over two years to write a guide that I was happy with since we did our initial side-channel attack talk using deep-learning at DEF CON 😊
