Methods from machine learning are used in the design of secure Industrial Control Systems.
Such methods focus on two major areas: detection of intrusions at the network level using …

The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …

The smart grid (SG) paradigm is the next technological leap of the conventional electrical
grid, contributing to the protection of the physical environment and providing multiple …

Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

Machine learning (ML)-based intrusion detection system (IDS) approaches have been
significantly applied and advanced the state-of-the-art system security and defense …

In this paper, we review and analyze intrusion detection systems for Agriculture 4.0 cyber
security. Specifically, we present cyber security threats and evaluation metrics used in the …

False data injection (FDI) attacks are crucial security threats to smart grid cyber-physical
system (CPS), and could result in cataclysmic consequences to the entire power system …

Multimicrogrid (MMG) systems have the potential to play an increasingly important role in the
transformation of existing power grid to smart grid. However, the open and distributed …

With the recent increase in internet usage, the number of important, sensitive, confidential
individual and corporate data passing through internet has increasingly grown. With gaps in …

Supervisory Control and Data Acquisition (SCADA) systems play an important role in
monitoring industrial processes such as electric power distribution, transport systems, water …