Signature-based Network Intrusion Detection Systems (NIDSs) have traditionally been used
to detect malicious traffic, but they are incapable of detecting new threats. As a result …

The increase of cyber attacks in both the numbers and varieties in recent years demands to
build a more sophisticated network intrusion detection system (NIDS). These NIDS perform …

Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …

Machine-learning based network intrusion detection systems (ML-NIDS) are increasingly
popular in the fight against network attacks. In particular, promising detection results have …

Network intrusion detection systems (NIDS) are an essential defense for computer networks
and the hosts within them. Machine learning (ML) nowadays predominantly serves as the …

Neural networks (NNs) are increasingly popular in developing NIDS, yet can prove
vulnerable to adversarial examples. Through these, attackers that may be oblivious to the …

Abstract Machine Learning (ML) can be incredibly valuable to automate anomaly detection
and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is …

Adversarial examples are inputs to a machine learning system intentionally crafted by an
attacker to fool the model into producing an incorrect output. These examples have achieved …

Machine learning (ML), especially deep learning (DL) techniques have been increasingly
used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has …

Showing a great potential in various domains, machine learning techniques are more and
more used in the task of malicious network traffic detection to significantly enhance the …