Network Intrusion Detection Systems (NIDSs) are crucial tools for ensuring cyber security.
Recently, machine learning-based NIDSs have gained popularity due to their ability to adapt …

Network Intrusion Detection Systems (NIDSs) using pattern matching have a fatal weakness
in that they cannot detect new attacks because they only learn existing patterns and use …

Machine learning (ML)-based network intrusion detection systems (NIDSs) depend entirely
on the performance of machine learning models. Therefore, many studies have been …

The network intrusion detection system needs to detect intrusions in real-time without delay
when an intrusion is attempted. To this end, various approaches, such as packet-based …

Network intrusion detection system plays a critical role in protecting the target network from
attacks. However, most existing detection methods cannot fully utilize the information …

Current intrusion detection techniques cannot keep up with the increasing amount and
complexity of cyber attacks. In fact, most of the traffic is encrypted and does not allow to …

Current flow-based Network Intrusion Detection Systems (NIDSs) have the drawback of
detecting attacks only once the flow has ended, resulting in potential delays in attack …

Network intrusion detection systems (NIDS) today must quickly provide visibility into
anomalous behavior on a growing amount of data. Meanwhile different data models have …

As internet usage rapidly increases in both private and corporate sectors, the study of
network intrusion detection is continuously becoming more relevant and has thus been …

Internet of Things (loT) has been playing an important role in many sectors, such as smart
cities, smart agriculture, smart healthcare, and smart manufacturing. However, loT de-vices …