Abstract Machine Learning (ML) algorithms are used to train the machines to perform
various complicated tasks that begin to modify and improve with experiences. It has become …

The significant growth in the use of the Internet and the rapid development of network
technologies are associated with an increased risk of network attacks. Network attacks refer …

Machine learning (ML) and deep learning (DL) have both produced overwhelming interest
and drawn unparalleled community interest recently. With a growing convergence of online …

A robust network intrusion detection system (NIDS) plays an important role in cyberspace
security for protecting confidential systems from potential threats. In real world network, there …

As network attacks are constantly and dramatically evolving, demonstrating new patterns,
intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques …

As part of Big Data trends, the ubiquitous use of the Internet of Things (IoT) in the industrial
environment has generated a significant amount of network traffic. In this type of IoT …

The goal of this systematic and broad survey is to present and discuss the main challenges
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …

Cybersecurity has been widely used in various applications, such as intelligent industrial
systems, homes, personal devices, and cars, and has led to innovative developments that …

The rapid growth of Internet of Things (IoT) is expected to add billions of IoT devices
connected to the Internet. These devices represent a vast attack surface for cyberattacks. For …

Anomaly-based machine learning-enabled intrusion detection systems (AML-IDSs) show
low performance and prediction accuracy while detecting intrusions in the Internet of Things …