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Masking has become one of the most effective approaches for securing hardware designs against side-channel attacks. Regardless of the effort put into correctly implementing masking schemes on a field-programmable gate array (FPGA), leakage can be unexpectedly observed. This is due to the fact that the assumption underlying all masked designs, i.e., the leakages of different shares are independent of each other, may no longer hold in practice. In this regard, extreme temperatures have been shown to be an important factor in inducing leakage, even in correctlymasked designs. This has previously been verified using an external heat generator (i.e., a climate chamber). In this paper, we examine whether the leakage can be induced using the circuit components themselves without making any changes to the design. Specifically, we target masked neural networks (NNs) in FPGAs, one of the main building blocks of which is block random access memory (BRAM). In this respect, thanks to the inherent characteristics of NNs, our novel internal heat generators leverage solely the memories devoted to storing the user’s input, especially when frequently writing alternating patterns into BRAMs. The possibility of observing first-order leakage is evaluated by considering one of the most recent and successful first-order secure masked NNs, namely ModuloNET. ModuloNET is specifically designed for FPGAs, where BRAMs are used to store inputs and intermediate computations. Our experimental results demonstrate that undesirable first-order leakage can be observed and exploited by increasing the temperature when an alternating input is applied to the masked NN. To give a better understanding of the impact of extreme heat, we further perform a similar test on the design using an external heat generator, where a similar conclusion can be drawn.

 

A universal circuit (UC) can be thought of as a programmable circuit that can simulate any circuit up to a certain size by specifying its secret configuration bits. UCs have been incorporated into various applications, such as private function evaluation (PFE). Recently, studies have attempted to formalize the concept of semiconductor intellectual property (IP) protection in the context of UCs. This is despite the observations made in theory and practice that, in reality, the adversary may obtain additional information about the secret when executing cryptographic protocols. This paper aims to answer the question of whether UCs leak information unintentionally, which can be leveraged by the adversary to disclose the configuration bits. In this regard, we propose the first photon emission analysis against UCs relying on computer vision-based approaches. We demonstrate that the adversary can utilize a cost-effective solution to take images to be processed by off-the-shelf algorithms to extract configuration bits. We examine the efficacy of our method in two scenarios: (1) the design is small enough to be captured in a single image during the attack phase, and (2) multiple images should be captured to launch the attack by deploying a divide-and-conquer strategy. To evaluate the effectiveness of our attack, we use metrics commonly applied in side-channel analysis, namely rank and success rate. By doing so, we show that our profiled photon emission analysis achieves a success rate of 1 by employing a few templates (concretely, only 18 images were used as templates).

 

Profiled side-channel analysis (SCA) leverages leakage from cryptographic implementations to extract the secret key. When combined with advanced methods in neural networks (NNs), profiled SCA can successfully attack even those cryptocores assumed to be protected against SCA. Despite the rise in the number of studies devoted to NN-based SCA, a range of questions has remained unanswered, namely: how to choose an NN with an adequate configuration, how to tune the NN’s hyperparameters, when to stop the training, etc. Our proposed approach, “InfoNEAT,” tackles these issues in a natural way. InfoNEAT relies on the concept of neural structure search, enhanced by information-theoretic metrics to guide the evolution, halt it with novel stopping criteria, and improve time-complexity and memory footprint. The performance of InfoNEAT is evaluated by applying it to publicly available datasets composed of real side-channel measurements. In addition to the considerable advantages regarding the automated configuration of NNs, InfoNEAT demonstrates significant improvements over other approaches for effective key recovery in terms of the number of epochs (e.g.,x6 faster) and the number of attack traces compared to both MLPs and CNNs (e.g., up to 1000s fewer traces to break a device) as well as a reduction in the number of trainable parameters compared to MLPs (e.g., by the factor of up to 32). Furthermore, through experiments, it is demonstrated that InfoNEAT’s models are robust against noise and desynchronization in traces. 

A widely-regarded approach in Printed Circuit Board (PCB) reverse engineering (RE) uses non-destructive Xray computed tomography (CT) to produce three-dimensional volumes with several slices of data corresponding to multi-layered PCBs. The noise sources specific to X-ray CT and variability from designers make it difficult to acquire the features needed for the RE process. Hence, these X-ray CT images require specialized image processing techniques to examine the various features of a single PCB to later be translated to a readable CAD format. Previously, we presented an approach where the Hough Circle Transform was used for initial feature detection, and then an iterative false positive removal process was developed specifically for detecting vias on PCBs. Its performance was compared to an off-the-shelf application of the Mask Region-based Convolutional Network (M-RCNN). M-RCNN is an excellent deep learning approach that is able to localize and classify numerous objects of different scales within a single image. In this paper, we present a version of M-RCNN that is fine-tuned for via detection. Changes include polygon boundary annotations on the single X-ray images of vias for training and transfer learning to leverage the full potential of the network. We discuss the challenges of detecting vias using deep learning, our working solution, and our experimental procedure. Additionally, we provide a qualitative evaluation of our approach and use quantitative metrics to compare the proposed approach with the previous iterative one.