Biblio

Random number generators play a pivotal role in generating security primitives, e.g., encryption keys, nonces, initial vectors, and random masking for side-channel countermeasures. A quantum entropy source based on radioactive isotope decay can be exploited to generate random numbers with sufficient entropy. If a deterministic random bit generator (DRBG) is combined for post-processing, throughput of the quantum random number generator (QRNG) can be improved. However, general DRBGs are susceptible to side-channel attacks. In this paper, we propose a framework called SCR-QRNG framework, which offers Side-Channel Resistant primitives using QRNG. The QRNG provides sources of randomness for modulating the clock frequency of a DRBG to obfuscate side-channel leakages, and to generate unbiased random numbers for security primitives. The QRNG has robustness against power side-channel attacks and is in compliance with NIST SP 800-22/90B and BSI AIS 31. We fabricate a quantum entropy chip, and implement a PCB module for a random frequency clock generator and a side-channel resistant QRNG on an FPGA.

Modern embedded computing devices are vulnerable against malware and software piracy due to insufficient security scrutiny and the complications of continuous patching. To detect malicious activity as well as protecting the integrity of executable software, it is necessary to monitor the operation of such devices. In this paper, we propose a disassembler based on power-based side-channel to analyze the real-time operation of embedded systems at instruction-level granularity. The proposed disassembler obtains templates from an original device (e.g., IoT home security system, smart thermostat, etc.) and utilizes machine learning algorithms to uniquely identify instructions executed on the device. The feature selection using Kullback-Leibler (KL) divergence and the dimensional reduction using PCA in the time-frequency domain are proposed to increase the identification accuracy. Moreover, a hierarchical classification framework is proposed to reduce the computational complexity associated with large instruction sets. In addition, covariate shifts caused by different environmental measurements and device-to-device variations are minimized by our covariate shift adaptation technique. We implement this disassembler on an AVR 8-bit microcontroller. Experimental results demonstrate that our proposed disassembler can recognize test instructions including register names with a success rate no lower than 99.03% with quadratic discriminant analysis (QDA).