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Nowadays, there is an increasing interest in the development of AutonomousVehicles (AV). However, there are two types of attack challenges that canaffect AVs and are yet to be resolved, i.e., sensor attacks and vehicle accessattacks. This paper, to the best of our knowledge, is the first work thatproposes a novel authentication scheme involving DRAM power-up unique featuresusing deep Convolutional Neural Network (CNN), which can be used to implementsecure access control of autonomous vehicles. Our approach consists of twoparts. First, we convert raw power-up sequence data from DRAM cells into atwo-dimensional (2D) format to generate a DRAM image structure. Second, weapply deep CNN to DRAM images, in order to extract unique features from eachmemory to classify them for authentication. To evaluate our proposed approach,we utilize data from three Commercial-Off-The-Shelf (COTS) DRAMs taken undervarious environmental and other conditions (high/low temperature, high/lowsupply voltage and aging effects). Based on our results, our proposedauthentication method ``DRAMNet'' achieves 98.63% accuracy and 98.49%precision. In comparison to other state-of-the-art CNN architectures, such asthe AlexNet and VGGNet models, our DRAMNet approach fares equally well orbetter than them.

DRAMNet: Authentication based on Physical Unique Features of DRAM Using Deep Convolutional Neural Networks