Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead
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 …
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
A roadmap toward the resilient internet of things for cyber-physical systems
The Internet of Things (IoT) is a ubiquitous system connecting many different devices-the
things-which can be accessed from the distance. The cyber-physical systems (CPSs) …
things-which can be accessed from the distance. The cyber-physical systems (CPSs) …
Towards energy-efficient and secure edge AI: A cross-layer framework ICCAD special session paper
The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …
processed on regular basis has pushed processing to the edge of the computing systems …
[HTML][HTML] On misbehaviour and fault tolerance in machine learning systems
Abstract Machine learning (ML) provides us with numerous opportunities, allowing ML
systems to adapt to new situations and contexts. At the same time, this adaptability raises …
systems to adapt to new situations and contexts. At the same time, this adaptability raises …
[HTML][HTML] A lightweight cryptography (LWC) framework to secure memory heap in Internet of Things
The extensive networking of devices and the large amount of data generated from the
Internet of Things (IoT) has brought security issues to the attention of the researcher. Java is …
Internet of Things (IoT) has brought security issues to the attention of the researcher. Java is …
Qusecnets: Quantization-based defense mechanism for securing deep neural network against adversarial attacks
Adversarial examples have emerged as a significant threat to machine learning algorithms,
especially to the convolutional neural networks (CNNs). In this paper, we propose two …
especially to the convolutional neural networks (CNNs). In this paper, we propose two …
Attack detection based on machine learning techniques to safe and secure for CPS—A review
DM Sharma, SK Shandilya - … Conference on IoT, Intelligent Computing and …, 2023 - Springer
Technological progression in communication and computing domains has led to the advent
of cyber-physical systems (CPS). As an emerging technological advancement, CPS security …
of cyber-physical systems (CPS). As an emerging technological advancement, CPS security …
EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems
Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under
unsupervised settings and low operational power/energy due to their bio-plausible …
unsupervised settings and low operational power/energy due to their bio-plausible …
Security for machine learning-based systems: Attacks and challenges during training and inference
The exponential increase in dependencies between the cyber and physical world leads to
an enormous amount of data which must be efficiently processed and stored. Therefore …
an enormous amount of data which must be efficiently processed and stored. Therefore …
Overview of security for smart cyber-physical systems
The tremendous growth of interconnectivity and dependencies of physical and cyber
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …