With the exponentially evolving trends in technology, IoT networks are vulnerable to serious
security issues, allowing intruders to break into networks without authorization and …

Network intrusion detection (NID) aims to detect various network attacks and is an important
task for guaranteeing network security. However, existing NID methods usually require a …

Machine learning has become a promising technology for anomaly detection of cyber-
physical systems (CPSs). However, the trained anomaly detection models always suffer from …

This research focuses on the crucial task of accurately forecasting electricity consumption, a
key concern in modern societies where electricity is essential for industries, healthcare, and …

To address the problems of attack category omission and poor generalization ability of
traditional Intrusion Detection System (IDS) when processing unbalanced input data, an …

Intrusion detection is an essential task for protecting the cyber environment from attacks.
Many studies have proposed sophisticated models to detect intrusions from a large amount …

Cyber-security experts often require the help of an automated process that filters and
classifies network attacks. To apply specific preventive measures for securing networks, the …

Due to the severe imbalance in the quantities of normal samples and attack samples, as well
as among different types of attack samples, intrusion detection systems suffer from low …

Agile network security is paramount in our modern world which is currently dominated by
Internet systems and expanding digital spaces. This rapid digital transformation has created …

Network Intrusion Detection Systems (NIDS) are powerful tools for identifying and deterring
cybersecurity attacks nowadays. However, while these modern IDS can detect typical …