差分侧信道密码分析中泄露模型的线性回归分析

从统计学线性回归模型的角度研究密码设备差分侧信道分析攻击中泄露模型的建模及估计,在不需对设备信息泄露有提前了解的情况下,得出线性回归泄露模型,克服了传统泄露模型的局限性.首先,分析能耗泄露的随机模型从而构建线性回归模型,然后用最小二乘估计和最小一乘估计两种方法求解线性回归模型的系数,最后基于八位控制器PayTV-AES智能卡平台实现能耗泄露的建模及系数估计.通过对两种求解方法结果的比较,提出最小二乘估计比最小一乘估计更适合用于泄露模型的线性回归分析;通过对被估模型系数曲线的分析,提出线性回归分析可以用于测量数据的预处理,以提高泄露模型建模效率.

Linear Regression Analysis for Leakage Model of Differential Side Channel Cryptanalysis

An advanced statistical method i.e. linear regression model is proposed to construct the power leakage model for the differential side-channel-analysis (DSCA) attacks on cryptographic devices. Even with only a very limited knowledge on how the device leaks information, the linear regression leakage model can be constructed, which overcomes the limitations of the traditional leakage models. First, the stochastic approach for analysis of power leakage is investigated and the linear regression model is built. Then the coefficients of the linear regression model are estimated with two methods: LSE (least square estimator) and LAE (least absolute estimator). Finally the mathematical model and methods are realized by an experimental analysis of an AES implementation on an 8-bit microcontroller based PayTV Smartcard Platform. After the comparative analysis of the both estimators, it is pointed out that LSE is more suitable than LAE concerning the linear regression analysis of leakage model. In addition, investigation on the curves of the estimated model coefficients shows that linear regression analysis can be applied to preprocess the measurement traces and the preprocessing helps to increase the efficiency of leakage modeling.