The staggering development of cyber threats has propelled experts, professionals and
specialists in the field of security into the development of more dependable protection …

Anomaly-based network intrusion detection systems are highly significant in detecting
network attacks. Robust machine learning and deep learning models for identifying network …

With the continuous occurrence of cybersecurity incidents, network intrusion detection has
become one of the most critical issues in cyber ecosystems. Although previous machine …

The introduction of deep learning techniques in intrusion detection problems has enabled
an enhanced standard of detection effectiveness. However, most of the progress has …

Modern network intrusion detection systems always utilize deep learning to improve their
intelligence and feature learning abilities. To overcome the difficulties of accessing a large …

The cyber-attacks have recently posed a threat to national security; meanwhile, the
pervasiveness of malware and cyber terrorism encumbers the beneficial utilization of the …

Nowadays, it is common for applications to require servers to run constantly and aim as
close as possible to zero downtime. The slightest failure might cause significant financial …

Network intrusion detection is an important technology for maintaining cybersecurity. The
inherent difficulties co-existing in network traffic datasets, such as class imbalance, class …

The use of network connected devices has grown exponentially in recent years
revolutionizing our daily lives. However, it has also attracted the attention of cybercriminals …

Abstract Network Intrusion Detection Systems (NIDSs) are crucial for resisting cyber threats.
However, NIDSs equipped with supervised learning models do not generalize well to …