Information Theory-based Evolution of Neural Networks for Side-channel Analysis
Information Theory-based Evolution of Neural Networks for Side-channel Analysis
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DOI：
10.46586/tches.v2023.i1.401-437
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
R. Acharya;F. Ganji;Domenic Forte
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文献类型：
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作者：
R. Acharya;F. Ganji;Domenic Forte
分析侧通道分析（SCA）利用加密实现的泄漏来提取密钥。当与神经网络（NN）中的高级方法相结合时，分析的SCA可以成功地攻击那些被认为受到SCA保护的加密核心。尽管致力于基于NN的SCA的研究数量有所增加，但仍有一系列问题没有得到回答，即：如何选择具有足够配置的NN，如何调整NN的超参数，何时停止训练等。InfoNEAT依赖于神经结构搜索的概念，通过信息理论指标来指导进化，用新的停止标准停止进化，并改善时间复杂度和内存占用。InfoNEAT的性能进行评估，将其应用到公开的数据集组成的真实的侧信道测量。除了关于NN的自动化配置的相当大的优点之外，InfoNEAT在时期的数量方面证明了对用于有效密钥恢复的其他方法的显著改进（例如，x6更快）以及与MLP和CNN相比的攻击痕迹数量（例如，多达1000个更少的破坏设备的迹线）以及与MLP相比可训练参数的数量的减少（例如，高达32倍）。此外，通过实验，它表明InfoNEAT的模型是强大的抗噪声和去噪的痕迹。
Profiled side-channel analysis (SCA) leverages leakage from cryptographic implementations to extract the secret key. When combined with advanced methods in neural networks (NNs), profiled SCA can successfully attack even those cryptocores assumed to be protected against SCA. Despite the rise in the number of studies devoted to NN-based SCA, a range of questions has remained unanswered, namely: how to choose an NN with an adequate configuration, how to tune the NN’s hyperparameters, when to stop the training, etc. Our proposed approach, “InfoNEAT,” tackles these issues in a natural way. InfoNEAT relies on the concept of neural structure search, enhanced by information-theoretic metrics to guide the evolution, halt it with novel stopping criteria, and improve time-complexity and memory footprint. The performance of InfoNEAT is evaluated by applying it to publicly available datasets composed of real side-channel measurements. In addition to the considerable advantages regarding the automated configuration of NNs, InfoNEAT demonstrates significant improvements over other approaches for effective key recovery in terms of the number of epochs (e.g.,x6 faster) and the number of attack traces compared to both MLPs and CNNs (e.g., up to 1000s fewer traces to break a device) as well as a reduction in the number of trainable parameters compared to MLPs (e.g., by the factor of up to 32). Furthermore, through experiments, it is demonstrated that InfoNEAT’s models are robust against noise and desynchronization in traces.