Low Complexity Correlation Power Analysis by Combining Power Trace Biasing and Correlation Distribution Techniques

Abstract

Side channel attack (SCA) is a class of crypt-analytic attacks for security evaluation of cryptographic and embedded microprocessor implementations. Among several SCA approaches, the correlation power analysis (CPA) is an efficient way to recover the secret key of the specific cryptographic algorithms running on the target devices such as embedded microprocessors. However, the evaluation process is time-consuming since a large number of traces are required to overcome the impact of noise. Hence, this paper proposes new methods to reduce the computation time by using Point of Interest (POI) extractor with the power trace biasing technique and the correlation distribution for the low complexity correlation power analysis (CPA). The theoretical explanations are provided and the experiments on different platforms such as ASCAD and RISC-V processor based databases are conducted to justify the proposed techniques. Especially, our experiments are performed with different protected schemes such as masking, hiding and combined hiding-masking techniques. The experimental results indicate that our proposed methods provide reliable results in comparison with the standard CPA. By using only a half of the power traces for taking the POIs, our first proposal not only decreases the execution time approximately by half but also enhances the success rate of the attack. Moreover, the second method based on power trace biasing technique is proposed in order to achieve better results and reduce the number of traces needed for selecting the POIs. With only 28.9% of given power traces needed, our second proposed technique reduces the execution time to only 2.6 times of the standard CPA.INDEX TERMS Correlation power analysis, side channel attack, embedded microprocessor security, power trace biasing, correlation distribution.

Improve the Performance of Semi-Supervised Side-channel Analysis Using HWFilter Method

“…DL-SCA with unsupervised learning is suitable for scenarios where the key is unknown and a substantial volume of unlabeled power traces are available [5][6][7]. Although this type of DL-SCA exhibits significant advantages over traditional non-profiling SCAs such as DPA [8] and CPA [9], it still presents certain gaps when compared to DL-SCA with supervised learning. DL-SCA with semi-supervised learning overcomes the limitations of the previous two types of DL-SCA, enabling key recovery even when labeled power traces are scarce.…”