Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …

Abstract Information and Communication Technology (ICT) has a great impact on social
wellbeing, economic growth and national security in todays world. Generally, ICT includes …

Nowadays, there is a huge and growing concern about security in information and
communication technology among the scientific community because any attack or anomaly …

With increasing reliance on Internet of Things (IoT) devices and services, the capability to
detect intrusions and malicious activities within IoT networks is critical for resilience of the …

Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Network anomaly detection is an important and dynamic research area. Many network
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …

Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly
detection (AD). With the rapid increase in the number of Internet-connected devices, the …

Threats of distributed denial of service (DDoS) attacks have been increasing day-by-day due
to rapid development of computer networks and associated infrastructure, and millions of …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …