Online Performance Evaluation of Deep Learning Networks for Side-Channel Analysis
Online Performance Evaluation of Deep Learning Networks for Side-Channel Analysis
复制标题
用于侧信道分析的深度学习网络的在线性能评估
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Amaury Habrard
中科院分区：
文献类型：
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作者：
Damien Robissout;Gabriel Zaid;Brice Colombier;L. Bossuet;Amaury Habrard
在过去的几年里，基于深度学习的边信道分析越来越受欢迎。为了理解用于执行攻击的神经网络的内部工作原理，已经做了很多工作，还有很多工作要做。然而，寻找一个合适的度量来评估神经网络的能力是一个开放的问题，在许多文章中都有讨论。我们通过引入一个专门用于侧信道分析的在线评估指标来解决这个问题，并使用它来对文献中发现的现有卷积神经网络执行早期停止。该指标比较网络在训练集和验证集上的性能，以检测欠拟合和过拟合。因此，我们通过找到网络的最佳训练历元来提高网络的性能，从而将使用的轨迹数量减少30%。对于大多数考虑的网络，训练时间也减少了。
Deep learning based side-channel analysis has seen a rise in popularity over the last few years. A lot of work is done to understand the inner workings of the neural networks used to perform the attacks and a lot is still left to do. However, finding a metric suitable for evaluating the capacity of the neural networks is an open problem that is discussed in many articles. We propose an answer to this problem by introducing an online evaluation metric dedicated to the context of side-channel analysis and use it to perform early stopping on existing convolutional neural networks found in the literature. This metric compares the performance of a network on the training set and on the validation set to detect underfitting and overfitting. Consequently, we improve the performance of the networks by finding their best training epoch and thus reduce the number of traces used by 30%. The training time is also reduced for most of the networks considered.