As “deep neural networks”(DNNs) achieve increasing accuracy, they are getting employed
in increasingly diverse applications, including security-critical applications such as medical …

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 …

Recent work on stealing machine learning (ML) models from inference engines with
physical side-channel attacks warrant an urgent need for effective side-channel defenses …

Training accurate deep learning (DL) models require large amounts of training data,
significant work in labeling the data, considerable computing resources, and substantial …

Machine learning (ML) models can be trade secrets due to their development cost. Hence,
they need protection against malicious forms of reverse engineering (eg, in IP piracy). With a …

Deep Neural Network (DNN) workloads are quickly moving from datacenters onto edge
devices, for latency, privacy, or energy reasons. While datacenter networks can be protected …

The last decade has witnessed remarkable research advances at the intersection of
machine learning (ML) and hardware security. The confluence of the two technologies has …

Edge-based IoT devices have experienced phenomenal growth in recent years due to
rapidly increasing demand in various emerging applications which typically utilize machine …

Convolutional Neural Networks (CNNs) are widely used in various domains, including
image recognition, medical diagnosis and autonomous driving. Recent advances in …

Deep neural networks (DNNs) have been applied to various industries. In particular, DNNs
on embedded devices have attracted considerable interest because they allow real-time …