Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the
emerging Internet of Things (IoT) has gained a lot of attention from the government …

Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …

Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …

Globally, the external internet is increasingly being connected to industrial control systems.
As a result, there is an immediate need to protect these networks from a variety of threats …

Energy storage units (ESUs) enable several attractive features of modern smart grids such
as enhanced grid resilience, effective demand response, and reduced bills. However …

Intrusion detection systems are evolving into intelligent systems that perform data analysis
searching for anomalies in their environment. The development of deep learning …

In advanced metering infrastructure (AMI) networks, smart meters installed at the consumer
side should report fine-grained power consumption readings (every few minutes) to the …

Globally, the external Internet is increasingly being connected to the contemporary industrial
control system. As a result, there is an immediate need to protect the network from several …

Network intrusion detection systems are evolving into intelligent systems that perform data
analysis while searching for anomalies in their environment. Indeed, the development of …

Internet of things (IoT) devices are prone to attacks due to the limitation of their privacy and
security components. These attacks vary from exploiting backdoors to disrupting the …