AI/ML-based intrusion detection systems (IDSs) and misbehavior detection systems (MDSs)
have shown great potential in identifying anomalies in the network traffic of networked …

Intrusion detection systems (IDS) are a very vital part of network security, as they can be
used to protect the network from illegal intrusions and communications. To detect malicious …

Intrusion detection systems (IDSs) are an essential cog of the network security suite that can
defend the network from malicious intrusions and anomalous traffic. Many machine learning …

Utilizing machine learning (ML)-based approaches for network intrusion detection systems
(NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to …

In cybersecurity, a network intrusion detection system (NIDS) is a critical component in
networks. It monitors network traffic and flags suspicious activities. To effectively detect …

As communication technology advances, various and heterogeneous data are
communicated in distributed environments through network systems. Meanwhile, along with …

With the rapid advancement in machine learning (ML), ML-based Intrusion Detection
Systems (IDSs) are widely deployed to protect networks from various attacks. One of the …

Machine learning (ML), especially deep learning (DL) techniques have been increasingly
used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has …

Network anomaly detection systems (NADSs) play a significant role in every network
defense system as they detect and prevent malicious activities. Therefore, this paper offers …

Cybercriminals continually develop innovative strategies to confound and frustrate their
victims, necessitating constant vigilance to protect the availability, confidentiality, and …