Accelerating Mutual Information Analysis based Power Analysis Attacks Using GPU

Side Channel Attacks are a popular modern cryptanalysis technique used by adversaries in embedded devices to break the security key. In these types of attacks, the attackers are keen on identifying the weaknesses of the physical implementation of the cryptosystem and utilize such vulnerabilities to extract the key. Power Analysis Attack is a form of Side Channel Attack in which, the adversary exploits power consumed by a cryptographic device during encryption to obtain the key. Mutual Information Analysis (MIA) is a concept introduced in information theory that measures the dependence between two random variables. In MIA based Power Analysis Attack, mutual information between two random variables is taken as the side channel distinguisher. Here, the two variables are physical leakages of the device and the power model based on key estimates. Since this method has more advantages to attackers compared to other methods, it is vital for cryptanalysts to find better countermeasures against this. But, due to the lack of efficient implementations it is hard for cryptanalysts to do that kind of research. In this paper, we present a methodology to accelerate MIA based Power Analysis Attacks using a GPU (Graphical Processor Unit) like NVIDIA Compute Unified Device Architecture (CUDA). Our proposed method promises to better utilize the capabilities of NVIDIA CUDA and obtain a speedup of more than 100 times compared to its sequential version.