Machine learning and deep learning techniques are widely used to evaluate intrusion
detection systems (IDS) capable of rapidly and automatically recognizing and classifying …

Attacks on networks are currently the most pressing issue confronting modern society.
Network risks affect all networks, from small to large. An intrusion detection system must be …

The use of deep learning models for the network intrusion detection task has been an active
area of research in cybersecurity. Although several excellent surveys cover the growing …

Many studies utilized machine learning schemes to improve network intrusion detection
systems recently. Most of the research is based on manually extracted features, but this …

Abstract The Internet of Things (IoT) has achieved exponential growth worldwide. Although
the IoT is used by millions of users, these networks are handicapped by attacks such as …

In recent years, all real-world processes have been shifted to the cyber environment
practically, and computers communicate with one another over the Internet. As a result, there …

Network intrusion detection systems (IDS) examine network packets and alert system
administrators and investigators to low-level security violations. In large networks, these …

Machine learning techniques are being widely used to develop an intrusion detection
system (IDS) for detecting and classifying cyberattacks at the network-level and the host …

Intrusion detection systems (IDS) play a critical role in safeguarding computer networks
against unauthorized access and malicious activities. However, traditional IDS approaches …

The network intrusion detection system is an important tool for protecting computer networks
against threats and malicious attacks. Many techniques have recently been proposed; …