The increasing number of security attacks have inspired researchers to employ various
classifiers, such as support vector machines (SVMs), to deal with them in Intrusion detection …

Network Intrusion Detection Systems (NIDS) are designed to safeguard the security needs of
enterprise networks against cyber-attacks. However, NIDS networks suffer from several …

The development of an anomaly-based intrusion detection system (IDS) is a primary
research direction in the field of intrusion detection. An IDS learns normal and anomalous …

With the rapid advancements of ubiquitous information and communication technologies, a
large number of trustworthy online systems and services have been deployed. However …

Anomaly-based intrusion detection systems (IDSs) have been deployed to monitor network
activity and to protect systems and the Internet of Things (IoT) devices from attacks (or …

The enormous increase in the use of the Internet in daily life has provided an opportunity for
the intruder attempt to compromise the security principles of availability, confidentiality, and …

Security is one of the main requirements of the current computer systems, and recently it
gains much importance as the number and severity of malicious attacks increase …

Network malicious traffic detection is one of the essential tasks of computer networks, which
has become an obstacle to network development as networks are expanding in size and …

As an essential part of the network-based intrusion detection systems (IDS), malicious traffic
detection using deep learning methods has become a research focus in network intrusion …

Abstract Machine learning (ML) algorithms are often used to design effective intrusion
detection (ID) systems for appropriate mitigation and effective detection of malicious cyber …