Network security becomes indispensable to our daily interactions and networks. As attackers
continue to develop new types of attacks and the size of networks continues to grow, the …

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 …

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 …

Intrusion detection is the core topic of network security, and the intrusion detection algorithm
based on deep learning has become a research hotspot in network security. In this paper, a …

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 …

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

Nowadays, computer networks and the Internet are unprotected from many security threats.
Introducing adaptive and flexible security-related techniques is challenging because of the …

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

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 …

Network security has always been a hot topic as security and reliability are vital to software
and hardware. Network intrusion detection system (NIDS) is an effective solution to the …