Publicly available datasets are an indispensable tool for researchers, as they allow testing
new algorithms on a wide range of different scenarios and making scientific experiments …

Introduction: The growth of ubiquitous networked devices and the proliferation of
geographically dispersed 'Internet of Thing'devices have exponentially increased network …

This paper presents a new Network Intrusion Detection System (NIDS) based on Graph
Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks …

Abstract Network Intrusion Detection Systems (NIDSs) are important tools for the protection
of computer networks against increasingly frequent and sophisticated cyber attacks …

Healthcare is one of the key areas of prospect for the Internet of Things (IoT). To facilitate
better medical services, enormous growth in the field of the Internet of Medical Things (IoMT) …

In recent years, many methods for intrusion detection systems (IDS) have been designed
and developed in the research community, which have achieved a perfect detection rate …

SQL injections rank in the OWASP Top 3. The literature shows that analyzing network
datagrams allows for detecting or preventing such attacks. Unfortunately, such detection …

Deep learning (DL) greatly enhances binary anomaly detection capabilities through effective
statistical network characterization; nevertheless, the intrusion class differentiation …

Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …

Intrusion detection is a critical aspect of network security to protect computer systems from
unauthorized access and attacks. The capacity of traditional intrusion detection systems …