DRAMNet：使用深度卷积神经网络基于DRAM物理唯一特征的身份验证
DRAMNet: Authentication based on Physical Unique Features of DRAM Using Deep Convolutional Neural Networks
摘要：
如今，人们对自动驾驶汽车（AV）的发展越来越感兴趣。然而，有两种类型的攻击挑战可能会影响AV，并且尚未解决，即，传感器攻击和车辆访问攻击。据我们所知，这篇论文是第一篇提出一种新的身份验证方案的工作，该方案涉及使用深度卷积神经网络（CNN）的DRAM上电独特功能，可用于实现自动驾驶汽车的安全访问控制。我们的方法包括两个部分。首先，我们将来自DRAM单元的原始上电序列数据转换成二维（2D）格式以生成DRAM图像结构。其次，我们将深度CNN应用于DRAM图像，以便从每个存储器中提取独特的特征来对其进行分类以进行身份验证。为了评估我们提出的方法，我们利用三个商业现货（COTS）DRAM在各种环境和其他条件下（高/低温，高/低电源电压和老化效应）的数据。基于我们的结果，我们提出的认证方法'' DRAMNet ''达到98.63%的准确率和98.49%的精度。与其他最先进的CNN架构（如AlexNet和VGGNet模型）相比，我们的DRAMNet方法与它们一样好或更好。
英文摘要：
Nowadays, there is an increasing interest in the development of Autonomous Vehicles (AV). However, there are two types of attack challenges that can affect AVs and are yet to be resolved, i.e., sensor attacks and vehicle access attacks. This paper, to the best of our knowledge, is the first work that proposes a novel authentication scheme involving DRAM power-up unique features using deep Convolutional Neural Network (CNN), which can be used to implement secure access control of autonomous vehicles. Our approach consists of two parts. First, we convert raw power-up sequence data from DRAM cells into a two-dimensional (2D) format to generate a DRAM image structure. Second, we apply deep CNN to DRAM images, in order to extract unique features from each memory to classify them for authentication. To evaluate our proposed approach, we utilize data from three Commercial-Off-The-Shelf (COTS) DRAMs taken under various environmental and other conditions (high/low temperature, high/low supply voltage and aging effects). Based on our results, our proposed authentication method ``DRAMNet'' achieves 98.63% accuracy and 98.49% precision. In comparison to other state-of-the-art CNN architectures, such as the AlexNet and VGGNet models, our DRAMNet approach fares equally well or better than them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
