Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …

Machine learning has become widely adopted as a strategy for dealing with a variety of
cybersecurity issues, ranging from insider threat detection to intrusion and malware …

Smart grids have emerged as a transformative technology in the power sector, enabling
efficient energy management. However, the increased reliance on digital technologies also …

The rapid advancement of artificial intelligence within the realm of cybersecurity raises
significant security concerns. The vulnerability of deep learning models in adversarial …

Fog computing has arisen to complement cloud computing, offering a cost‐effective
architecture to power the Internet of things. Fog computing is a network computing and …

Abstract Artificial Intelligence (AI)-based IDS systems are susceptible to adversarial attacks
and face challenges such as complex evaluation methods, elevated false positive rates …

Internet of Things (IoT) technologies serve as a backbone of cutting-edge intelligent
systems. Machine Learning (ML) paradigms have been adopted within IoT environments to …

Agile network security is paramount in our modern world which is currently dominated by
Internet systems and expanding digital spaces. This rapid digital transformation has created …

Intrusion detection systems (IDS) are a very vital part of network security, as they can be
used to protect the network from illegal intrusions and communications. To detect malicious …

Machine Learning (ML) techniques have been applied over the past two decades to improve
the abilities of Intrusion Detection Systems (IDSs). Over time, several enhancements have …