Federated Learning (FL) has become very popular since it enables clients to train a joint model collaboratively without sharing their private data. However, FL has been shown to be susceptible to backdoor and inference attacks. While in the former, the adversary injects manipulated updates into the aggregation process; the latter leverages clients' local models to deduce their private data. Contemporary solutions to address the security concerns of FL are either impractical for real-world deployment due to high-performance overheads or are tailored towards addressing specific threats, for instance, privacy-preserving aggregation or backdoor defenses. Given these limitations, our research delves into the advantages of harnessing the FPGA-based computing paradigm to overcome performance bottlenecks of software-only solutions while mitigating backdoor and inference attacks. We utilize FPGA-based enclaves to address inference attacks during the aggregation process of FL. We adopt an advanced backdoor-aware aggregation algorithm on the FPGA to counter backdoor attacks. We implemented and evaluated our method on Xilinx VMK-180, yielding a significant speed-up of around 300 times on the IoT-Traffic dataset and more than 506 times on the CIFAR-10 dataset.@en

The efficiency of the profiling side-channel analysis can be significantly improved with machine learning techniques. Although powerful, a fundamental machine learning limitation of being data-hungry received little attention in the side-channel community. In practice, the maximum number of leakage traces that evaluators/attackers can obtain is constrained by the scheme requirements or the limited accessibility of the target. Even worse, various countermeasures in modern devices increase the conditions on the profiling size to break the target. This work demonstrates a practical approach to dealing with the lack of profiling traces. Instead of learning from a one-hot encoded label, transferring the labels to their distribution can significantly speed up the convergence of guessing entropy. By studying the relationship between all possible key candidates, we propose a new metric, denoted Label Correlation (LC), to evaluate the generalization ability of the profiling model. We validate LC with two common use cases: early stopping and network architecture search, and the results indicate its superior performance.@en

This paper uses RISC-V vector extensions to speed up lattice-based operations in architectures based on HW/SW co-design. We analyze the structure of the number-theoretic transform (NTT), inverse NTT (INTT), and coefficient-wise multiplication (CWM) in CRYSTALS-Kyber, a lattice-based key encapsulation mechanism. We propose 12 vector extensions for CRYSTALS-Kyber multiplication and four for finite field operations in combination with two optimizations of the HW/SW interface. This results in a speed-up of 141.7, 168.7, and 245.5 times for NTT, INTT, and CWM, respectively, compared with the baseline implementation, and a speed-up of over four times compared with the state-of-the-art HW/SW co-design using RV32IMC.@en

The usage of deep learning in profiled side-channel analysis requires a careful selection of neural network hyperparameters. In recent publications, different network architectures have been presented as efficient profiled methods against protected AES implementations. Indeed, completely different convolutional neural network models have presented similar performance against public side-channel traces databases. In this work, we analyze how weight initializers’ choice influences deep neural networks’ performance in the profiled side-channel analysis. Our results show that different weight initializers provide radically different behavior. We observe that even high-performing initializers can reach significantly different performance when conducting multiple training phases. Finally, we found that this hyperparameter is more dependent on the choice of dataset than other, commonly examined, hyperparameters. When evaluating the connections with other hyperparameters, the biggest connection is observed with activation functions.@en

Electronic devices have permeated into all aspects of our lives, from basic smart cards to sophisticated hybrid automobile systems. These devices comprise a range of products like sensors, wearable gadgets, mobile phones, personal computers, and others, playing vital roles in many applications and enabling the Internet of Things (IoT). However, with this interconnectedness comes the associated security risks since attackers can exploit vulnerabilities in the system. Securing electronic devices requires the use of cryptographic algorithms and trusted execution environments (TEEs). Cryptographic algorithms ensure data confidentiality and integrity through encryption/decryption, hashing, and digital signatures. TEEs provide secure enclaves within the system for critical operations that prevent unauthorized modifications and access by imposing stringent access restrictions. These two measures have become robust mechanisms for enhancing the security of critical operations and data access control. Despite the above security measures, electronic systems are susceptible to various attacks, including side-channel analysis (SCA), in which attackers exploit information leakage from physical devices while executing instructions or cryptographic algorithms. Power consumption and electromagnetic radiation (EM) are common indicators of this leakage. Countermeasures such as masking and hiding techniques are commonly employed to enhance resistance against SCA. However, the advent of deep learning in SCA has brought forth new challenges, rendering previously efficient countermeasures ineffective. Moreover, deep learning-based SCA has the potential to eliminate preprocessing and alignment requirements inherent in earlier methods. Therefore, this thesis focuses on two main objectives. The first objective is the implementation of cryptographic algorithms and the incorporation of TEEs for secure-sensitive applications. HW/SW co-design approach will be utilized to attain optimal performance while preserving flexibility. The second objective of this thesis is the investigation of deep learning-based SCA to explore its effectiveness in detecting side-channel vulnerabilities.@en