Intrusion detection systems are an essential part of any cybersecurity architecture. These
systems are critical in defending networks against a variety of security threats. In recent …

Adversarial machine learning is a recent area of study that explores both adversarial attack
strategy and detection systems of adversarial attacks, which are inputs specially crafted to …

Network intrusion detection plays an important role in the Internet of Things systems for
protecting devices from security breaches. Facing challenges of the rapidly increasing …

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 …

Network security applications, including Intrusion Detection Systems (IDS) of deep neural
networks (DNN), are increasing rapidly to make detection task of anomaly activities more …

Due to their massive success in various domains, deep learning techniques are increasingly
used to design network intrusion detection solutions that detect and mitigate unknown and …

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

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

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 …