Analysis of improved side channel attack on AES-128 with masking - Tan, Ri Zheng (2019)
Encryption is a widely used practice of data security in both transit and storage, in many applications. The Advanced Encryption Standard (AES) is the gold standard of encryption and is rated to be highly secured. Side Channel Attack is a type of cyber-attack that can exploit and reveal the secret keys used for the AES encryption. Correlation Power Analysis (CPA) is the most efficient type of SCA, relative to the other SCA types, Simple Power Analysis (SPA) and Differential Power Analysis. Masking of ciphers is a form of countermeasure against SCA. It is particularly effective against CPA since its implementation decorrelates the power trace from the leakage model. AES-128 with masking is capable of resisting CPA. However, many methods to improve SCA have been proposed over the recent years. This research was conducted to analyse whether SCA implementations can be improved to break the AES-128 cipher with masking. A software designed for SCA was utilised, and tests were conducted on two attack scenarios. A last round Side Channel Attack using CPA was used against a Look-Up-Table (LUT) based AES S-Box implementation, with countermeasures and improvements being made on both ends as required. Vulnerability of the normal and masked AES was evaluated using Signal-to-Noise Ratio power trace analysis. Conclusions on the vulnerability of masked AES against SCA were made based on the results from the attack scenarios, and the observations made on the weak points of the AES encryption through SNR analysis. The experimental results verified the effectiveness of proposed masking scheme implemented on AES-128 against a CPA attack. While SCA was successful against an unprotected AES within 4000 traces of data, the proposed masked AES was highly robust against a CPA attack, where an adversary could not attack said implementation even after collecting 400% more traces of data for the attack. The proposed masked implementation was resilient against SCA with >16000 traces of data.