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

Feature selection plays a vital role in building machine learning models. Irrelevant features
in data affect the accuracy of the model and increase the training time needed to build the …

As the complexity of cyber-attacks keeps increasing, new robust detection mechanisms
need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be …

Intrusion detection systems (IDSs) are the fundamental parts of any network security
infrastructure given their role as layers of defense against hackers. However, IDSs generate …

To enhance the universal adaptability of the Real-Time deployment of Industry 5.0, various
machine learning-based cyber threat detection models are given in the literature. Most of the …

As the complexity of cyber-attacks keeps increasing, new and more robust detection
mechanisms need to be developed. The next generation of Intrusion Detection Systems …

With the exponential growth in connected smart devices that interchange sensitive, crucial,
and personal data over the Internet of Things (IoT)-based cyberspace, IoT becomes …

False positives (FPs) and false negatives (FNs) happen in every Intrusion Detection System
(IDS). How often they occur is regarded as a measurement of the accuracy of the system …

Anomaly-based network intrusion detection systems (A-NIDS) are an important and
essential defence mechanism against network attacks. However, they generate a high …

In the last few years there has been considerable increase in the efficiency of Intrusion
Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity …