GIPSim: Low Level Power Modeling for Resiliency in Side Channel Attack on GPUs
Graphic Processing Units (GPUs) are able to accelerate a wide range of applications. Given the massive number of compute cores on these devices, GPUs have become an attractive platform to accelerate security and cryptography applications. While performance is an important quality to prevent online attacks, current accelerators are ill-equipped to protect against side-channel attacks. This class of attacks exploits the physical implementation of a cryptographic algorithm, rather than the inherent theoretical weaknesses of the algorithm.
Power modeling of GPU devices has been well studied by the computer architecture community. It has been shown that by recording the amount of energy consumed by a GPU during encryption or decryption, an attacker can capture secret information. If we can understand how the underlying microarchitecture leaks side-channel information to an attacker, we can build much more robust obfuscation approaches.
In this thesis, our aim is not to build yet another GPU power model. Instead, we deliver GIPSim, a framework to enable security researchers to reason about side-channel leakage present in the context of a GPU execution-driven simulator. We show how researchers can capture detailed power estimates while running CUDA programs on a Kepler-family GPU and use the information to obfuscate power by adding noise to the power estimate. This, in turn, reduces the vulnerability present in this context. Our goal is to design a system that can thwart side-channel attacks. We demonstrate that we can model data-dependent power dissipation, capturing the hamming distance of data values used during the execution of AES encryption. This same approach is used in power-based side-channel attacks. GIPSim is one of the first simulation environments that can be used for evaluating power side-channel resiliency and help build a more secure system.