In this review article, we provide a comprehensive guide to the endeavor of problem
decomposition within the field of Genetic Programming (GP), specifically tree-based GP for …

Incomputer networks, Intrusion Detection has become a major concern. In network security,
various traditional techniques like intrusion prevention, cryptography and user …

In machine learning, ensemble techniques are widely used to improve the performance of
both classification and regression systems. They combine the models generated by different …

This paper proposes a heterogeneous ensemble classifier configuration for a multiclass
intrusion detection problem. The ensemble is composed of k-nearest neighbors, artificial …

Intrusion detection tools have largely benefitted from the usage of supervised classification
methods developed in the field of data mining. However, the data produced by modern …

Modern intrusion detection systems must handle many complicated issues in real-time, as
they have to cope with a real data stream; indeed, for the task of classification, typically the …

Cyber security classification algorithms usually operate with datasets presenting many
missing features and strongly unbalanced classes. In order to cope with these issues, we …

Unbalanced classes, the ability to detect changes in real-time, the speed of the streams and
other peculiar characteristics make most of the data mining algorithms not apt to operate …

Due to the radical change in both Chinese and global economic environment, it is essential
to develop a practical model to predict financial distress. The support vector machine (SVM) …

Classification is a relevant task in the cyber security domain, but it must be able to cope with
unbalanced and/or incomplete datasets and must also react in real-time to changes in the …