Deep learning techniques and algorithms are emerging as a disruptive technology with the
potential to transform global economies, environments and societies. They have been …

Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …

The increasing digitization and interconnection of legacy Industrial Control Systems (ICSs)
open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Critical infrastructure systems are evolving from isolated bespoke systems to those that use
general-purpose computing hosts, IoT sensors, edge computing, wireless networks and …

Although state estimation using a bad data detector (BDD) is a key procedure employed in
power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial …

The advent of Industry 4.0 has led to a rapid increase in cyber attacks on industrial systems
and processes, particularly on Industrial Control Systems (ICS). These systems are …

In smart homes, when an actuator's state changes, it sends an event notification to the IoT
hub to report this change (eg, the door is unlocked). Prior works have shown that event …

Smart Grid (SG) is the revolutionised power network characterised by a bidirectional flow of
energy and information between customers and suppliers. The integration of power …

Abstract Adversarial Machine Learning (AML) discusses the act of attacking and defending
Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …