The analysis of a substantial portion of network data is a requirement for almost any
machine learning-based network intrusion detection method. High dimension features, a …

Intrusion detection system (IDS) is a well-known and effective component of network security
that provides transactions upon the network systems with security and safety. Most of earlier …

Due to the insidious characteristics of network intrusion behaviors, developing an efficient
intrusion detection system is still a big challenge, especially in the era of big data where the …

Handling redundant and irrelevant features in high-dimension datasets has caused a long-
term challenge for network anomaly detection. Eliminating such features with spectral …

The problem of network intrusion detection poses innumerable challenges to the research
community, industry, and commercial sectors. Moreover, the persistent attacks occurring on …

Machine learning methods have been widely used in the field of intrusion detection.
However, most methods require labeled data sets, and the overhead is very high. Network …

In recent years, machine learning-based cyber intrusion detection methods have gained
increasing popularity. The number and complexity of new attacks continue to rise; therefore …

Traditional machine learning‐based intrusion detection often only considers a single
algorithm to identify intrusion data, lack of the flexibility method, low detection rate, no …

A large number of attacks are launched daily in the era of the internet and with a large
number of users. Nowadays, effective detection of numerous attacks using the Intrusion …

Intrusion detection systems (IDSs) play a significant role in the field of network security,
dealing with the ever-increasing number of network threats. Machine learning-based IDSs …