A New Key Rank Estimation Method to Investigate Dependent Key Lists of Side Channel Attacks
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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：PROCEEDINGS OF THE 2017 ASIAN HARDWARE ORIENTED SECURITY AND TRUST SYMPOSIUM (ASIANHOST)
摘要：Rank estimation algorithm (REA) is a useful post analysis tool to evaluate key recovery threat of real side-channel attacks. The existing rank estimation algorithms only consider the score lists of independent subkeys. This paper looks at dependent score lists, which correspond to the result of the key-recovery attack and the key-difference recovery attack, e.g. k(0), k(1) and k(0,1). First, we propose a new REA that can combine the dependent score lists called DK-REA. After selecting one subkey value, the rest subkey lists and key difference lists are combined to create new score lists for further key rank estimation in DK-REA. With simulated side-channel leakage of AES-128, we apply DK-REA to investigate the correct key rank when different score lists are combined. Our result shows that when the number of power traces is enough to obtain reliable results, merging more score lists leads to the rise of the correct key's rank up to 220. When the number of dependent score lists added is over a certain amount, the rank will drop with the added score lists.
是否译文：否
发表时间：2017-01-01
合写作者：Wang, Shuang,Li, Yang
通讯作者：Li, Yang,王箭