Benchmark datasets are heavily depended upon by the research community to validate
theoretical findings and track progression in the state-of-the-art. NIDS dataset creation …

The Internet of Medical Things (IoMT) has become an attractive playground to
cybercriminals because of its market worth and rapid growth. These devices have limited …

Cybersecurity is one of the great challenges of today's world. Rapid technological
development has allowed society to prosper and improve the quality of life and the world is …

Various machine learning algorithms have been applied to network intrusion classification
problems, including both binary and multi-class classifications. Despite the existence of …

Among the difficulties encountered in building datasets to evaluate intrusion detection tools,
a tricky part is the process of labelling the events into malicious and benign classes. The …

Network function virtualization (NFV) supports the rapid development of service function
chain (SFC), which efficiently connects a sequence of network virtual function instances …

The summarized traffic flow version of the Canadian Institute for Cybersecurity Intrusion
Detection Evaluation dataset created at the University of New Brunswick in 2017 is popular …

To address the problems of complex environment, limited device computational resources
and limited memory resources in the existing IoT, SELSTM, an intrusion detection system …

Network Intrusion Detection Systems (NIDS) are a fundamental tool in cybersecurity. Their
ability to generalize across diverse networks is a critical factor in their effectiveness and a …

Due to the increasing number of cyber-attacks, the necessity to develop efficient intrusion
detection systems (IDS) is more imperative than ever. In IDS research, the most effectively …