CAO Jiahua,WU Zhen,WANG Yi,et al.Side Channel Attack of S-box Power Randomization based on CNN-BPR[J].Journal of Chengdu University of Information Technology,2022,37(01):16-20.[doi:10.16836/j.cnki.jcuit.2022.01.003]
基于CNN-BPR的S-Box功耗随机化侧信道攻击
- Title:
- Side Channel Attack of S-box Power Randomization based on CNN-BPR
- 文章编号:
- 2096-1618(2022)01-0016-05
- Keywords:
- side channel attack; template attack; convolutional neural network; loss function; Bayesian personalized ranking
- 分类号:
- TP309
- 文献标志码:
- A
- 摘要:
- S-Box功耗随机化是一种对抗侧信道攻击的防御方案，该方案将设备加密过程中S-Box输出值功耗泄露的位置进行随机化处理，降低了中间值与能量消耗的相关性，使得基于固定位置进行能量分析的代价大幅增加。具备平移不变性的卷积神经网络在侧信道攻击上取得了显著的效果。为进一步提高其对S-Box功耗随机化防御方案的攻击能力，基于贝叶斯个性化排序的思想，提出一种更符合侧信道攻击原理的CNN-BPR模型。实验结果表明，与Softmax交叉熵损失模型相比，CNN-BPR模型在使用全部训练能迹用于模板攻击时，成功恢复密钥所需要的攻击能迹数量能够减少3%，当使用60%的训练能迹用于模板攻击时，减少的攻击能迹数量能够达到27%。
- Abstract:
- S-box power randomization is a defense scheme against side channel attack. This scheme randomizes the location of power leakage of S-box output value in the process of device encryption, and reduces the correlation between intermediate value and energy consumption, and greatly increases the cost of energy analysis based on fixed location. Convolutional neural network with translation invariance has achieved remarkable results in side channel attack. In order to further improve its attack ability against S-box power randomization defense scheme, a CNN-BPR model more in line with the principle of side channel attack is proposed based on the idea of Bayesian personalized ranking. The experimental results show that, compared with softmax cross-entropy loss model, when CNN-BPR model uses all training energy traces for template attack, the number of attack traces required to successfully recover the key can be reduced by 3%, and when 60% of the training energy traces are used for template attack, the number of attack energy traces can be reduced by 27%.
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备注/Memo
收稿日期:2021-09-27
基金项目:“十三五”国家密码发展基金资助项目(MMJJ20180224); 四川省重点研发资助项目(2019YFG0096)