Ablation Analysis for Multi-device Deep Learning-based Physical Side-channel Analysis

The use of deep learning-based side-channel analysis is an effective way of performing profiling attacks on power and electromagnetic leakages, even against targets protected with countermeasures. While many research articles have reported successful results, they typically focus on profiling and attacking a single device, assuming that...

Interpreting the effectiveness of antioxidants to increase the resilience of asphalt binders: A global interlaboratory study

The design and use of antioxidant additives to reduce or slow down the aging of asphalt binders can bring about tremendous benefits to the asphalt industry. Despite many isolated and scattered research efforts showing mixed results, the application of this science to engineering-based solutions has been limited due to variability in results and...

SCA Strikes Back: Reverse Engineering Neural Network Architectures using Side Channels

This paper was selected for Top Picks in Hardware and Embedded Security 2020 and it presents a physical side-channel attack aiming at reverse engineering neural networks implemented on an edge device. The attack does not need access to training data and allows for neural network recovery by feeding known random inputs. We successfully reverse...

CSI NN: Reverse engineering of neural network architectures through electromagnetic side channel

Machine learning has become mainstream across industries. Numerous examples prove the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using side-channel information such as timing and electromagnetic (EM) emanations. To this end, we consider multilayer perceptron and...

Make Some Noise: Unleashing the Power of Convolutional Neural Networks for Profiled Side-channel Analysis

Profiled side-channel analysis based on deep learning, and more precisely Convolutional Neural Networks, is a paradigm showing significant potential. The results, although scarce for now, suggest that such techniques are even able to break cryptographic implementations protected with countermeasures. In this paper, we start by proposing a new...

Study of Asphalt Binders Fatigue with a New Dynamic Shear Rheometer Geometry

With the effort to predict precisely the lifetime of asphalt binders and subsequently optimize their utilization in a more economical way, the objective of this study was to introduce a new methodology to improve the fatigue characterization of asphalt binders through a new dynamic shear rheometer (DSR) sample testing geometry. Initially,...

The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations

We concentrate on machine learning techniques used for profiled side-channel analysis in the presence of imbalanced data. Such scenarios are realistic and often occurring, for instance in the Hamming weight or Hamming distance leakage models. In order to deal with the imbalanced data, we use various balancing techniques and we show that most of...