Noncompact uniform universal approximation

The universal approximation theorem is generalised to uniform convergence on the (noncompact) input space Rⁿ. All continuous functions that vanish at infinity can be uniformly approximated by neural networks with one hidden layer, for all activation functions φ that are continuous, nonpolynomial, and asymptotically polynomial at ±∞...

On the use of ResNet architec- tures for Side-Channel Analysis

Some of the most prominent types of attacks against modern cryptographic implementations are side-channel attacks. These attacks leverage some unintended, often physical, leakage of the implementation to retrieve secret information. In recent times, a large part of the focus of side-channel research has been on deep learning methods. These...

IGA-Reuse-NET: A deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization[Formula presented]

In this paper, a deep learning framework combined with isogeometric analysis (IGA for short) called IGA-Reuse-Net is proposed for efficient reuse of numerical simulation on a set of topology-consistent models. Compared with previous data-driven numerical simulation methods only for simple computational domains, our method can predict high...

The Best of Two Worlds: Deep Learning-assisted Template Attack

In the last decade, machine learning-based side-channel attacks have become a standard option when investigating profiling side-channel attacks. At the same time, the previous state-of-the-art technique, template attack, started losing its importance and was more considered a baseline to compare against. As such, most of the results reported...

Exploring Feature Selection Scenarios for Deep Learning-based Side-channel Analysis

One of the main promoted advantages of deep learning in profiling side-channel analysis is the possibility of skipping the feature engineering process. Despite that, most recent publications consider feature selection as the attacked interval from the side-channel measurements is pre-selected. This is similar to the worst-case security...

Development of deep learning-based joint elements for thin-walled beam structures

This study presents a new modeling technique to estimate the stiffness matrix of a thin-walled beam-joint structure using deep learning. When thin-walled beams meet at joints, significant sectional deformations occur, such as warping and distortion. These deformations should be considered in the one-dimensional beam analysis, but it is...

Focus is Key to Success: A Focal Loss Function for Deep Learning-Based Side-Channel Analysis

The deep learning-based side-channel analysis represents one of the most powerful side-channel attack approaches. Thanks to its capability in dealing with raw features and countermeasures, it becomes the de facto standard approach for the SCA community. The recent works significantly improved the deep learning-based attacks from various...

To Overfit, or Not to Overfit: Improving the Performance of Deep Learning-Based SCA

Profiling side-channel analysis allows evaluators to estimate the worst-case security of a target. When security evaluations relax the assumptions about the adversary’s knowledge, profiling models may easily be sub-optimal due to the inability to extract the most informative points of interest from the side-channel measurements. When used for...

Reinforcement Learning-Based Design of Side-Channel Countermeasures

Deep learning-based side-channel attacks are capable of breaking targets protected with countermeasures. The constant progress in the last few years makes the attacks more powerful, requiring fewer traces to break a target. Unfortunately, to protect against such attacks, we still rely solely on methods developed to protect against generic...

Perception modelling by invariant representation of deep learning for automated structural diagnostic in aircraft maintenance: A study case using DeepSHM

Predictive maintenance, as one of the core components of Industry 4.0, takes a proactive approach to maintain machines and systems in good order to keep downtime to a minimum and the airline maintenance industry is not an exception to this. To achieve this goal, practices in Structural Health Monitoring (SHM) complement the existing Non...

On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis

In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural...

Remove Some Noise: On Pre-processing of Side-channel Measurements with Autoencoders

In the profiled side-channel analysis, deep learning-based techniques proved to be very successful even when attacking targets protected with countermeasures. Still, there is no guarantee that deep learning attacks will always succeed. Various countermeasures make attacks significantly more complex, and such countermeasures can be further...

Systematic Side-Channel Analysis of Curve25519 with Machine Learning

Profiling attacks, especially those based on machine learning, proved to be very successful techniques in recent years when considering the side-channel analysis of symmetric-key crypto implementations. At the same time, the results for implementations of asymmetric-key cryptosystems are very sparse. This paper considers several machine learning...

Deep Learning and Machine Learning in Robotics

Deep learning has gone through massive growth in recent years. In many fields—computer vision, speech recognition, machine translation, game playing, and others—deep learning has brought unprecedented progress and become the method of choice. Will the same happen in robotics and automation? In a sense, it is already happening. Today, deep...

Human Activity Recognition using Channel State Information

Human Activity Recognition (HAR) is a key enabler of various applications, including smart homes, health care, Internet of Things (IoT), and virtual reality games. A large number of HAR systems are based on wearable sensors and computer vision. However, a challenge that has emerged in the last few years entails recognizing human activities using...

Learning about risk: Machine learning for risk assessment

Risk assessment has a primary role in safety-critical industries. However, it faces a series of overall challenges, partially related to technology advancements and increasing needs. There is currently a call for continuous risk assessment, improvement in learning past lessons and definition of techniques to process relevant data, which are...