Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …

Intrusion detection is a key topic in cybersecurity. It aims to protect computer systems and
networks from intruders and malicious attacks. Traditional intrusion detection systems (IDS) …

Studies have shown the vulnerability of machine learning algorithms against adversarial
samples in image classification problems in deep neural networks. However, there is a need …

Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms
are being used in diverse domains where security is a concern, such as, automotive …

The increasing abundance of large high-quality datasets, combined with significant
technical advances over the last several decades have made machine learning into a major …

Traditional approaches in network intrusion detection follow a signature-based approach,
however the use of anomaly detection approaches based on machine learning techniques …

Machine Learning (ML) models are applied in a variety of tasks such as network intrusion
detection or Malware classification. Yet, these models are vulnerable to a class of malicious …

Abstract Machine learning-based intrusion detection systems (IDS) are essential security
functions in conventional and software-defined networks alike. Their success and the …

Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …

Intrusion Detection Systems (IDS) are increasingly adopting machine learning (ML)-based
approaches to detect threats in computer networks due to their ability to learn underlying …