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 …
Machine learning security attacks and defense approaches for emerging cyber physical applications: A comprehensive survey
The cyber physical systems integrate the sensing, computation, control and networking
processes into physical objects and infrastructure, which are connected through the Internet …
processes into physical objects and infrastructure, which are connected through the Internet …
Machine learning in cybersecurity: a comprehensive survey
Today's world is highly network interconnected owing to the pervasiveness of small personal
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …
Machine learning security: Threats, countermeasures, and evaluations
Machine learning has been pervasively used in a wide range of applications due to its
technical breakthroughs in recent years. It has demonstrated significant success in dealing …
technical breakthroughs in recent years. It has demonstrated significant success in dealing …
A survey on machine-learning based security design for cyber-physical systems
A cyber-physical system (CPS) is the integration of a physical system into the real world and
control applications in a computing system, interacting through a communications network …
control applications in a computing system, interacting through a communications network …
Sok: Security and privacy in machine learning
Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …
applications such as data analytics, autonomous systems, and security diagnostics. ML is …
Machine learning for hardware security: Opportunities and risks
R Elnaggar, K Chakrabarty - Journal of Electronic Testing, 2018 - Springer
Recently, machine learning algorithms have been utilized by system defenders and
attackers to secure and attack hardware, respectively. In this work, we investigate the impact …
attackers to secure and attack hardware, respectively. In this work, we investigate the impact …
Resilient machine learning for networked cyber physical systems: A survey for machine learning security to securing machine learning for CPS
Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …
Machine learning and the Internet of Things security: Solutions and open challenges
Abstract Internet of Things (IoT) is a pervasively-used technology for the last few years. IoT
technologies are also responsible for intensifying various everyday smart applications …
technologies are also responsible for intensifying various everyday smart applications …
Accurate, reliable and fast robustness evaluation
Throughout the past five years, the susceptibility of neural networks to minimal adversarial
perturbations has moved from a peculiar phenomenon to a core issue in Deep Learning …
perturbations has moved from a peculiar phenomenon to a core issue in Deep Learning …