Inter-Architecture Portability of Artificial Neural Networks and Side Channel Attacks
Inter-Architecture Portability of Artificial Neural Networks and Side Channel Attacks
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人工神经网络的跨架构可移植性和侧信道攻击
DOI：
10.1145/3526241.3530356
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Roveda, Janet
中科院分区：
文献类型：
--
作者：
Gopale, Manoj;Ditzler, Gregory;Lysecky, Roman;Roveda, Janet
侧信道攻击（SCA）已经被研究了几十年，这导致了许多技术，使用统计模型来提取系统信息的侧信道。最近，机器学习已经显示出极大的希望，可以提高SCA暴露漏洞的能力。人工神经网络（ANN）可以有效地学习侧通道内特征之间的非线性关系。在本文中，我们提出了一种多架构数据聚合技术，用于分析具有嵌入式处理器的系统的功率轨迹，该处理器基于三种类型的深度NN，即多层感知器（MLP），卷积神经网络（CNN）和递归神经网络（RNN）。这是探索NN和SCA的体系结构间可移植性的首批工作之一。我们证明了神经网络的鲁棒性执行基于功率的SCA在多个架构配置与不同的架构特征，如L1/L2缓存的大小和关联性，以及系统内存大小。我们提供了一组全面的基准测试，以证明架构上相同的设备对于基于配置文件的SCA来说并不重要
Side-channel attacks (SCA) have been studied for several decades, which resulted in many techniques that use statistical models to extract system information from side channels. More recently, machine learning has shown significant promise to advance the ability for SCAs to expose vulnerabilities. Artificial neural networks (ANN) can effectively learn nonlinear relationships between features within a side channel. In this paper, we propose a multi-architecture data aggregation technique to profile power traces for a system with an embedded processor that is based on three types of deep NNs, namely, multi-layer perceptrons (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN). This is one of the first works to explore the inter-architecture portability of NNs and SCAs. We demonstrate the robustness of the ANNs performing power-based SCAs on multiple architecture configurations with different architectural features, such as L1/L2 caches' size and associativity, and system memory size. We provide a comprehensive set of benchmarks to demonstrate that architecturally identical devices are not essential for profile-based SCAs
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
发表时间：
2015
期刊：
2017 International Joint Conference on Neural Networks (IJCNN)
影响因子：
--
作者：
Zdenek Martinasek;L. Malina;K. Trasy
通讯作者：
K. Trasy