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

TL;DR

This paper introduces DRAMNet, a novel authentication scheme for autonomous vehicles that uses deep CNNs to analyze DRAM power-up features converted into images, achieving high accuracy and precision.

Contribution

It is the first to propose using DRAM power-up features with deep CNNs for secure vehicle access control, demonstrating superior performance over existing CNN models.

Findings

Achieves 98.63% accuracy in authentication

Achieves 98.49% precision in authentication

Performs better or equal to AlexNet and VGGNet

Abstract

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.