In this paper, we present a review of recent advances in wrapper feature selection
techniques for attack detection and classification, applied in intrusion detection area. Due to …

Abstract Intrusion Detection Systems (IDSs) have become essential to the sound operations
of networks. These systems have the potential to identify and report deviations from normal …

Conventional intrusion detection systems based on supervised learning techniques require
a large number of samples for training, while in some scenarios, such as zero-day attacks …

Nowadays attacks on computer networks continue to advance at a rate outpacing cyber
defenders' ability to write new attack signatures. This paper illustrates a deep learning …

In this paper, Big Data and Deep Learning Techniques are integrated to improve the
performance of intrusion detection systems. Three classifiers are used to classify network …

The recent boom in deep learning has revealed that the application of deep neural networks
is a valuable way to address network intrusion detection problems. This paper presents a …

Networks had an increasing impact on modern life since network cybersecurity has become
an important research field. Several machine learning techniques have been developed to …

Context There are many datasets for training and evaluating models to detect web attacks,
labeling each request as normal or attack. Web attack protection tools must provide …

The aim of this study is to provide the numerical outcomes of a nonlinear HIV infection
system of latently infected CD4+ T cells exists in bioinformatics using Morlet wavelet (MW) …

Compared to the past, developments in computer and communication technologies have
provided extensive and advanced changes. The usage of new technologies provide great …