Abstract The Internet of Things (IoT) is increasingly utilized in daily life and industrial
production, particularly in critical infrastructures. IoT cybersecurity has an effect on people's …

As the digital landscape expands rapidly due to technological advancements, cybersecurity
concerns have become more prevalent. Intrusion Detection Systems (IDSs), which are …

Abstract The Internet of Things (IoT) ecosystem needs Intrusion Detection Systems (IDS) to
mitigate cyberattacks and exploit security vulnerabilities. Over the past years, utilizing …

Machine learning (ML)-based Network Intrusion Detection Systems (NIDSs) can classify
each network's flow behavior as benign or malicious by detecting heterogeneous features …

The increasing prevalence of the Industrial Internet of Things (IIoT) in industrial
environments amplifies the potential for security breaches and compromises. To monitor IIoT …

Abstract Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to
analyze data. In both, the data is stored centrally. The data involved may be sensitive and …

Intelligent Transport Systems (ITS) have provided new technologies to protect human life,
speed up assistance, and improve traffic, to aid drivers, passengers, and pedestrians …

Cyberattacks on Unmanned Aerial Vehicles (UAVs) have grown over the years due to the
increased popularity of these vehicles. These attacks may involve interrupting control …

Many sensitive applications require that data remain confidential and undisclosed, even for
intrusion detection objectives. For this purpose, the detection of anomalies in encrypted data …

The number of papers on network intrusion detection based on machine and deep learning
is growing at an unprecedented rate. Most of these papers follow a well-consolidated …