This chapter provides an overview of recent applications of deep learning to profiled side-channel analysis (SCA). The advent of deep neural networks (mainly multiple layer perceptrons and convolutional neural networks) as a learning algorithm for profiled SCA opened several new directions and possibilities to explore the occurrence of side-channel leakages from different categories of systems. This is particularly important for designers to verify to what extent an adversary can extract sensitive information when possessing state-of-the-art attack methods. Deep learning is a fast-evolving technology that provides several advantages in profiled SCA and we summarize what are the main directions and results obtained by the research community.
This chapter provides a general overview of AI methods used to support the design of cryptographic primitives and protocols. After giving a brief introduction to the basic concepts underlying the field of cryptography, we review the most researched use cases concerning the use of AI techniques and models to design cryptographic primitives, focusing mainly on Boolean functions, S-boxes and pseudorandom number generators. We then point out two interesting directions for further research on the design of cryptographic primitives where AI methods could be applied in the future.
Device or machine fingerprinting is the process of collecting information on a (part of a) device for its identification. This can be done under different scenarios and using information from different hardware and software layers of the device. Hardware fingerprinting typically refers to device fingerprinting using information collected from the hardware layer. Hardware fingerprinting can have nefarious usages related to privacy abuse, as well as many positive ones such as soft authentication, indoor positioning systems, and others. Here we introduce some of the uses of hardware fingerprinting, with special emphasis on those related to commonly available devices, and explain how machine learning and deep learning have enabled and/or improved them. Additionally, we discuss some of their limitations and possibilities for improvement.
In this chapter, we are considering the physical security of Machine Learning (ML) implementations on Edge Devices. We list the state-of-the-art known physical attacks, with the main attack objectives to reverse engineer and misclassify ML models. These attacks have been reported for different target platforms with the usage of both passive and active attacks. The presented works highlight the potential threat of stealing an intellectual property or confidential model trained with private data, and also the possibility to tamper with the device during the execution to cause misclassification. We also discus possible countermeasures to mitigate such attacks.
