Abstract Machine Learning (ML) techniques are becoming an invaluable support for network
intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats …

Abstract Machine learning algorithms efficiently trained on intrusion detection datasets can
detect network traffic capable of jeopardizing an information system. In this study, we use the …

The identification of unusual data in computer networks is a critical task for intrusion
detection systems. In this study, a novel approach has been proposed for improving …

The dramatic increase in the network traffic data has become a major concern in security
systems. Intrusion detection systems (TDSs), as common widely used security systems for …

The benchmark KDD dataset for intrusion detection system generated a decade ago has
become outdated as it does not reflect modern normal behaviors and contemporary …

The imbalance and multi-dimension are two common problems in the medical image
datasets, which affect the performances of the image processing procedures. The traditional …

Classification analysis is widely used in enhancing the quality of healthcare applications by
analysing data and discovering hidden patterns and relationships between the features …

Currently, there is a growing need from health entities for the integration of the use of
technology. Cardiovascular disease (CEI) identification systems allow a large extent to …

Feature selection in data mining enables the identification of significant features constituting
the given data. It facilitates identification and isolation of profitable features to ensure quality …

This paper aims to classify network traffic in order to segregate normal and anomalous
traffic. There can be multiple classes of network attacks, so a multiclass model is …