This article describes how sequential data modeling is a relevant task in Cybersecurity.
Sequences are attributed temporal characteristics either explicitly or implicitly. Recurrent …

As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a
variety of fields including industry, national defense, and healthcare. Traditional intrusion …

Recently, Convolutional neural network (CNN) architectures in deep learning have achieved
significant results in the field of computer vision. To transform this performance toward the …

In recent years, machine learning-based intrusion detection systems (IDSs) have proven to
be effective; especially, deep neural networks improve the detection rates of intrusion …

The prevention of intrusion is deemed to be a cornerstone of network security. Although
excessive work has been introduced on network intrusion detection in the last decade …

Can machine learning techniques solve our computer security problems and finally put an
end to the cat-and-mouse game between attackers and defenders? Or is this hope merely …

With the rapid advancements of ubiquitous information and communication technologies, a
large number of trustworthy online systems and services have been deployed. However …

We claim that modelling network traffic as a time series with a supervised learning approach,
using known genuine and malicious behaviour, improves intrusion detection. To …

Network security is subject to malicious attacks from multiple sources, and intrusion
detection systems play a key role in maintaining network security. During the training of …

Network intrusion detection system (NIDS) is a tool used to detect and classify the network
breaches dynamically in information and communication technologies (ICT) systems in both …