Why Deep Learning Makes it Difficult to Keep Secrets in FPGAs

Abstract

With the growth of popularity of Field-Programmable Gate Arrays (FPGAs) in cloud environments, new paradigms such as FPGA-as-a-Service (FaaS) emerge. This challenges the conventional FPGA security models which assume trust between the user and the hardware owner. In an FaaS scenario, the user may want to keep data or FPGA configuration bitstream confidential in order to protect privacy or intellectual property. However, securing FaaS use cases is hard due to the difficulty of protecting encryption keys and other secrets from the hardware owner. In this paper we demonstrate that even advanced key provisioning and remote attestation methods based on Physical Unclonable Functions (PUFs) can be broken by profiling side-channel attacks employing deep learning. Using power traces from two profiling FPGA boards implementing an arbiter PUF, we train a Convolutional Neural Network (CNN) model to learn features corresponding to “0” and “1” PUF’s responses. Then, we use the resulting model to classify responses of PUFs implemented in FPGA boards under attack (different from the profiling boards). We show that the presented attack can overcome countermeasures based on encrypting challenges and responses of a PUF.