The Circle of DL-SCA: Improving Deep Learning-based Side-channel Analysis

For almost three decades, side-channel analysis has represented a realistic and severe threat to embedded devices' security. As a well-known and influential class of implementation attacks, side-channel analysis has been applied against cryptographic implementations, processors, communication systems, and, more recently, machine learning models....

Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission data

Composite structures are highly valued for their strength-to-weight ratio, durability, and versatility, making them ideal for a variety of applications, including aerospace, automotive, and infrastructure. However, potential damage scenarios like impact, fatigue, and corrosion can lead to premature failure and pose a threat to safety. This...

Advanced Health Monitoring of Composite Structures Through Deep Learning-Based Analysis of Lamb Wave Data for Developing Health Indicators

A health indicator (HI) serves as an intermediary link between structural health monitoring (SHM) data and prognostic models, and an efficient HI should meet prognostic criteria, i.e., monotonicity, trendability, and prognosability. However, designing a proper HI for composite structures is a challenging task due to the complex damage...

Deep Learning in Maritime Autonomous Surface Ships: Current Development and Challenges

Autonomous surface ships have become increasingly interesting for commercial maritime sectors. Before deep learning (DL) was proposed, surface ship autonomy was mostly model-based. The development of artificial intelligence (AI) has prompted new challenges in the maritime industry. A detailed literature study and examination of DL...

A Cost-Sensitive Machine Learning Model With Multitask Learning for Intrusion Detection in IoT

A problem with machine learning (ML) techniques for detecting intrusions in the Internet of Things (IoT) is that they are ineffective in the detection of low-frequency intrusions. In addition, as ML models are trained using specific attack categories, they cannot recognize unknown attacks. This article integrates strategies of cost-sensitive...

Continuous Human Activity Recognition With Distributed Radar Sensor Networks and CNN–RNN Architectures

Unconstrained human activities recognition with a radar network is considered. A hybrid classifier combining both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for spatial–temporal pattern extraction is proposed. The 2-D CNNs (2D-CNNs) are first applied to the radar data to perform spatial feature extraction on the...

Human Motion Recognition With Limited Radar Micro-Doppler Signatures

The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume of the available training data. In this article, to tackle the issue of insufficient training data for HMR, we propose an instance-based transfer learning (ITL) method with limited radar micro-Doppler (MD)...