Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

The rapid advances in the internet and communication fields have resulted in a huge
increase in the network size and the corresponding data. As a result, many novel attacks are …

Recent developments in maintenance modelling fueled by data-based approaches such as
machine learning (ML), have enabled a broad range of applications. In the automotive …

The increasing trend toward using the Internet of Things (IoT) increased the number of
intrusions and intruders annually. Hence, the integration, confidentiality, and access to …

Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …

The increase in internet usage brings security problems with it. Malicious software can affect
the operation of the systems and disrupt data confidentiality due to the security gaps in the …

To protect the network, resources, and sensitive data, the intrusion detection system (IDS)
has become a fundamental component of organizations that prevents cybercriminal …

The Controller Area Network (CAN) is the most widely used in-vehicle communication
protocol, which still lacks the implementation of suitable security mechanisms such as …

Deep learning (DL) has gained popularity in network intrusion detection, due to its strong
capability of recognizing subtle differences between normal and malicious network activities …

Abstract Machine Learning (ML)-based Network Intrusion Detection Systems (NIDSs) have
become a promising tool to protect networks against cyberattacks. A wide range of datasets …