An intrusion detection system (IDS) is an important protection instrument for detecting
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …

With the Internet's unprecedented growth and nations' reliance on computer networks, new
cyber‐attacks are created every day as means for achieving financial gain, imposing …

Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data
confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a …

Abstract Machine-learning techniques have been actively employed to information security
in recent years. Traditional rule-based security solutions are vulnerable to advanced attacks …

The massive growth of data that are transmitted through a variety of devices and
communication protocols have raised serious security concerns, which have increased the …

From a security perspective, the research of the jeopardized 6G wireless communications
and its expected ultra-densified ubiquitous wireless networks urge the development of a …

The development of robust anomaly-based network detection systems, which are preferred
over static signal-based network intrusion, is vital for cybersecurity. The development of a …

Abstract Machine learning algorithms efficiently trained on intrusion detection datasets can
detect network traffic capable of jeopardizing an information system. In this study, we use the …

The exponential growth in computer networks and network applications worldwide has been
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …

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 …