This paper focus on multiple CNN-based (Convolutional Neural Network) models for COVID-
19 forecast developed by our research team during the first French lockdown. In an effort to …

Companies seek to promote a swift digitalization of their business processes and new
disruptive features to gain an advantage over their competitors. This often results in a wider …

Intrusion Detection System (IDS) is used to identify malicious traffic on the network. Apart
from rule-based IDS, machine learning and deep learning based on IDS are also being …

Network intrusion detection is a crucial cyber-security problem, where machine learning is
recognised as a relevant approach to detect signs of malicious activity in the network traffic …

Community networks (CNs) have become an important paradigm for providing essential
Internet connectivity in unserved and underserved areas across the world. However, an …

The impact of the Internet of Things (IoT) evolves rapidly, increasing the volume of traffic,
and complicating the management of large scalable networks. Despite the security tools …

In today's network environments, vulnerable to cyber threats such as hackers and viruses,
intrusion detection technology is considered the most effective means of detection and …

With the increased use of network technologies like Internet of Things (IoT) in many real-
world applications, new types of cyberattacks have been emerging. To safeguard critical …

In this study, we proposed the Gated Recurrent Unit method to develop an Intrusion
Detection System to detect traffic anomalies. This model was trained in several scenarios to …

An intrusion detection system is an automated monitoring tool that analyzes network traffic
and detects malicious activities by looking out either for known patterns of attacks or for an …