Feature Article: Consumer Electronics SecurityElectronics DRAM-Based Authentication Using Deep Convolutional Neural Networks Michael Yue Santa Clara University Nima Karimian San Jose State University Wei Yan Washington University in St. Louis Nikolaos Athanasios Anagnostopoulos University of Passau Fatemeh Tehranipoor Santa Clara University Abstract-Authentication is the act of proving that an integrated circuit (IC) is not counterfeit. One application of a physical unclonable function (PUF) circuit is to authenticate the identity of the chip using raw bits of the memory. However, several previous works present machine learning-based modeling attacks on PUFs. To alleviate this issue, we propose a novel authentication scheme involving unique DRAM power-up values using a deep convolutional neural network (CNN). This methodology eliminates the need for PUFs and can authenticate DRAM technology accurately with a neural network. Our approach converts raw power-up sequence data from DRAM cells into a twodimensional (2D) format to generate a DRAM image structure. This makes it harder for an adversary to use machine learning since there is no PUF to exploit the weaknesses. Then, we apply deep CNN to DRAM images to extract unique features from each chip and classify them for authentication. Our method " DRAMNet " achieves 98.84% accuracy and 98.73% precision. The proposed technique has the advantage of a faster authentication Digital Object Identifier 10.1109/MCE.2020.3002528 Date ofpublication 15 June 2020; date ofcurrent version 10 June 2021. 8 2162-2248 ß 2020 IEEE Published by the IEEE Consumer Technology Society IEEE Consumer Electronics Magazine