Profiling Power Analysis Attack Based on Multi-layer Perceptron Network
Profiling Power Analysis Attack Based on Multi-layer Perceptron Network
复制标题
基于多层感知网络的攻击功率分析分析
DOI：
10.1007/978-3-319-15765-8_18
复制
发表时间：
2015
期刊：
影响因子：
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通讯作者：
K. Trasy
中科院分区：
文献类型：
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作者：
Zdenek Martinasek;L. Malina;K. Trasy
2013年，在Martinasek和Zeman(RadioEngineering 22(2)，IF 0.687,2013年)和Martinasek等人的报告中提出了一种创新的功率分析方法。智能卡研究和高级应用。计算机科学讲义。施普林格国际出版公司，纽约，2014年)。实现的实验证明，基于多层感知器(MLP)的方法可以提供几乎100%的成功率。这种基于一阶成功率的描述是不够恰当的。此外，上述工作还存在其他不足：没有将MLP与其他著名的攻击进行比较，对手使用了太多的能量跟踪点，并且没有对MLP方法进行一般性描述。在本文中，我们通过引入基于MLP和模板的能量分析攻击的首次公平比较来消除这些弱点。通过使用相同的数据集、兴趣点的数量和猜测熵作为度量来完成比较。所创建的第一数据集包含不受保护的AES实现的功率轨迹，以便对存储的秘密密钥进行分类。第二和第三数据集独立于对应于屏蔽的AES实施(DPA竞赛v4)的公共可用电源轨迹而创建。在这个实验中，秘密偏移量的揭示取决于兴趣点的数量和功率迹线。此外，我们还创建了对MLP攻击的一般描述。
In 2013, an innovative method of power analysis was presented in Martinasek and Zeman (Radioengineering 22(2), IF 0.687, 2013) and Martinasek et al. (Smart Card Research and Advanced Applications. Lecture Notes in Computer Science. Springer International Publishing, New York, 2014). Realized experiments proved that the proposed method based on Multi-Layer Perceptron (MLP) can provide almost 100 % success rate. This description based on the first-order success rate is not appropriate enough. Moreover, the above mentioned works contain other lacks: the MLP has not been compared with other well-known attacks, an adversary uses too many points of power trace and a general description of the MLP method was not provided. In this paper, we eliminate these weaknesses by introducing the first fair comparison of power analysis attacks based on the MLP and templates. The comparison is accomplished by using the identical data sets, number of interesting points and guessing entropy as a metric. The first data set created contains the power traces of an unprotected AES implementation in order to classify the secret key stored. The second and third data sets were created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4). Secret offset is revealed depending on the number of interesting points and power traces in this experiment. Moreover, we create a general description of the MLP attack.