ADVANCED LOW-COST ELECTRO-MAGNETIC AND MACHINE LEARNING SIDE-CHANNEL ATTACKS

Side-channel analysis (SCA) is a prominent tool to break mathematically secure cryptographic engines, especially on resource-constrained devices. SCA attacks utilize physical leakage vectors like the power consumption, electromagnetic (EM) radiation, timing, cache hits/misses, that reduce the complexity of determining a secret key drastically, going from 2¹²⁸ for brute force attacks to 2¹² for SCA in the case of AES-128. Additionally, EM SCA attacks can be performed non-invasively without any modifications to the target under attack, unlike power SCA. To develop defenses against EM SCA, designers must evaluate the cryptographic implementations against the most powerful side-channel attacks. In this work, systems and techniques that improve EM side-channel analysis have been explored, making it lower-cost and more accessible to the research community to develop better countermeasures against such attacks. The first chapter of this thesis presents SCNIFFER, a platform to perform efficient end-to-end EM SCA attacks. SCNIFFER introduces leakage localization – an often-overlooked step in EM attacks – into the loop of an attack. Following SCNIFFER, the second chapter presents a practical machine learning (ML) based EM SCA attack on AES-128. This attack addresses issues dealing with low signal-to-noise ratio (SNR) EM measurements, proposing training and pre-processing techniques to perform an efficient profiling attack. In the final chapter, methods for mapping from power to EM measurements, are analyzed, which can enable training a ML model with much lower number of encryption traces. Additionally, SCA evaluation of high-level synthesis (HLS) based cryptographic algorithms is performed, along with the study of futuristic neural encryption techniques.