CRJan 3, 2018

Power Analysis Based Side Channel Attack

arXiv:1801.00932v120 citations
Originality Incremental advance
AI Analysis

This work addresses security vulnerabilities in cryptographic devices like smart cards, with incremental improvements in attacking new algorithms and countermeasures.

The researchers tackled the security threat of power analysis side-channel attacks by building a testbed for research, developing novel methods to break the Speck cryptographic algorithm in under an hour, and evaluating countermeasures, identifying a vulnerability in pseudo-random seed generation and proposing a hardware-based solution.

Power analysis is a branch of side channel attacks where power consumption data is used as the side channel to attack the system. First using a device like an oscilloscope power traces are collected when the cryptographic device is doing the cryptographic operation. Then those traces are statistically analysed using methods such as Correlation Power Analysis (CPA) to derive the secret key of the system. Being possible to break Advanced Encryption Standard (AES) in few minutes, power analysis attacks have become a serious security issue for cryptographic devices such as smart card. As the first phase of our project, we build a testbed for doing research on power analysis attacks. As power analysis is a practical type of attack in order to do any research, a testbed is the first requirement. Since building a test bed is a complicated process, having a pre-built testbed would save the time of future researchers. The second phase of our project is to attack the latest cryptographic algorithm called Speck which has been released by National Security Agency (NSA) for use in embedded systems. In spite it has lot of differences to AES making impossible to directly use the power analysis approach used for AES, we introduce novel approaches to break Speck in less than an hour. In the third phase of the project, we select few already introduced countermeasures and practically attack them on our testbed to do a comparative analysis. We show that software countermeasures such as random instruction injection and randomly shuffling S-boxes are good enough for their simplicity and cost. But we identify the possible threat due to the problem of generating a good seed for the pseudo-random algorithm running on the microcontroller. We attempt to address this issue by using a hardware-based true random generator that amplifies a random electrical signal and samples to generate a proper seed.

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