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

The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity
challenges has gained traction in industry and academia, partially as a result of widespread …

In recent years, many methods for intrusion detection systems (IDS) have been designed
and developed in the research community, which have achieved a perfect detection rate …

Abstract Network Intrusion Detection (NID) systems are one of the most powerful forms of
defense for protecting public and private networks. Most of the prominent methods applied to …

The potential for an intelligent transportation system (ITS) has been made possible by the
growth of the Internet of things (IoT) and artificial intelligence (AI), resulting in the integration …

The past few years have witnessed a substantial increase in cyberattacks on Internet of
Things (IoT) devices and their networks. Such attacks pose a significant threat to …

The uses of machine learning (ML) technologies in the detection of network attacks have
been proven to be effective when designed and evaluated using data samples originating …

Artificial intelligence (AI) and Machine Learning (ML) are widely employed to make the
solutions more accurate and autonomous in many smart and intelligent applications in the …

The industrial internet of things (IIoT) is an evolutionary extension of the traditional Internet of
Things (IoT) into processes and machines for applications in the industrial sector. The IIoT …

The frequency of cyber-attacks on the Internet of Things (IoT) networks has significantly
increased in recent years. Anomaly-based network intrusion detection systems (NIDSs) offer …