Deep learning tasks cover a broad range of domains and an even more extensive range of
applications, from entertainment to extremely safety-critical fields. Thus, Deep Neural …

Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …

Abstract Machine learning plays a significant role in today's business sectors and
governments, in which it is becoming more utilized as tools to help in decision making and …

Recent studies show that adversarial attacks on neural network weights, aka, Bit-Flip Attack
(BFA), can degrade Deep Neural Network's (DNN) prediction accuracy severely. In this …

Due to their proven efficiency, machine-learning systems are deployed in a wide range of
complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as …

Abstract The recent revealed Bit-Flip Attack (BFA) against deep neural networks (DNNs) is
highly concerning, as it can completely mislead the inference of quantized DNNs by only …

Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-
physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their …

Deep Learning has enabled machines to learn computational models (ie, Deep Neural
Networks-DNNs) that can perform certain complex tasks with claims to be close to human …

The tremendous growth of interconnectivity and dependencies of physical and cyber
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …

The detection of anomalies during the operation of deep neural networks (DNNs) is of
essential importance in safety-critical applications, such as autonomous vehicles. In the …