Power Analysis Based Side Channel Attack

TL;DR

This paper develops a power analysis attack testbed, introduces novel methods to break the Speck cipher, and evaluates countermeasures, highlighting the effectiveness of certain software and hardware defenses against side channel attacks.

Contribution

It presents a new testbed for power analysis research, novel attack techniques for Speck, and a comparative analysis of countermeasures including hardware-based random seed generation.

Findings

Power analysis can break Speck in less than an hour.

Software countermeasures like instruction injection and S-box shuffling are effective.

Hardware-based true random generators improve seed security.

Abstract

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.