In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

Intrusion detection system (IDS) is one of extensively used techniques in a network topology
to safeguard the integrity and availability of sensitive assets in the protected systems …

Transfer-learning methods aim to improve performance in a data-scarce target domain using
a model pretrained on a data-rich source domain. A cost-efficient strategy, linear probing …

Meta-heuristics are problem-independent optimization techniques which provide an optimal
solution by exploring and exploiting the entire search space iteratively. These techniques …

More and more network traffic data have brought great challenge to traditional intrusion
detection system. The detection performance is tightly related to selected features and …

This survey aims at providing a state-of-the-art overview of feature selection and fusion
strategies, which select and combine multi-view features effectively to accomplish …

Abstract The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that
was shown to have excellent performance for numerous optimization problems. In this …

Intrusions constitute one of the main issues in computer network security. Through malicious
actions, hackers can have unauthorised access that compromises the integrity, the …

The all-relevant problem of feature selection is the identification of all strongly and weakly
relevant attributes. This problem is especially hard to solve for time series classification and …