  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.
