Artificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate
repetitive tasks, accelerate threat detection and response, and improve the accuracy of their …

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

Network intrusion detection systems (NIDS) play a crucial role in maintaining network
security. However, current NIDS techniques tend to neglect the topological structures of …

Network intrusion detection remains a challenging research area as it involves learning from
large-scale imbalanced multiclass datasets. While machine learning algorithms have been …

Detection and prevention of intrusions in enterprise networks and systems is an important,
but challenging problem due to extensive growth and usage of networks that are constantly …

Abstract Network-based Intrusion Detection Systems (NIDSs) are deployed in computer
networks to identify intrusions. NIDSs analyse network traffic to detect malicious content …

This work serves two key objectives. First, it markedly supplements and extends the well-
known AWID corpus by capturing and studying traces of a wide variety of attacks hurled in …

Intrusion detection system (IDS) plays a significant role in preventing network attacks and
plays a vital role in the field of national security. At present, the existing intrusion detection …

The expeditious growth of the World Wide Web and the rampant flow of network traffic have
resulted in a continuous increase of network security threats. Cyber attackers seek to exploit …

Nowadays, detecting anomaly events in communication networks is highly under
consideration by many researchers. In a large communication network, traffic is massive …