The rapid evolution of cyber threats has made traditional intrusion detection systems (IDS)
increasingly ineffective in addressing sophisticated attacks. To combat this challenge, the …

Research in the field of Intrusion Detection is focused on developing an efficient strategy that
can identify network attacks. One of the important strategies is to supervise the network …

Deep learning (DL) is gaining significant prevalence in every field of study due to its
domination in training large data sets. However, several applications are utilizing machine …

The rapid growth and advances in communication and Internet technologies result in
efficient business transactions, communications as well as collaborations. However, this …

Intrusions in computer networks have increased significantly in the last decade, due in part
to a profitable underground cyber-crime economy and the availability of sophisticated tools …

This article describes an experimental investigation into the inter-dataset generalization of
supervised machine learning methods, trained to distinguish between benign and several …

Intrusion detection is one of the most common approaches for addressing security attacks in
modern networks. However, given the increasing diversity of attack behaviors, efficient …

Harnessing machine learning (ML) for advanced threat detection in cybersecurity represents
a pivotal evolution in defending against increasingly sophisticated digital threats. ML …

Networks play important roles in modern life, and cyber security has become a vital research
area. An intrusion detection system (IDS) which is an important cyber security technique …

The rise of cyber threats has necessitated the development of advanced detection
techniques to safeguard sensitive information and infrastructure. This research paper …