The rapid rise of cyberattacks and the gradual failure of traditional defense systems and
approaches led to using Machine Learning (ML) techniques to build more efficient and …

One of the key challenges of the machine learning (ML) based intrusion detection system
(IDS) is the expensive computation time which is largely caused by the redundant …

Computer networks intrusion detection systems (IDSs) and intrusion prevention systems
(IPSs) are critical aspects that contribute to the success of an organization. Over the past …

Abstract Network Intrusion Detection is the process of monitoring the events occurring in a
computer system or the network and analyzing them for the signs of possible intrusions. An …

The expansion of the internet has not only opened up new possibilities for cooperation,
networking, and ingenuity, but also presented fresh security hazards and complexities. As …

Many of the proposed machine learning (ML) based network intrusion detection systems
(NIDSs) achieve near perfect detection performance when evaluated on synthetic …

Abstract Network Intrusion Detection Systems (NIDS) are essential to combat security threats
in network environments. These systems monitor and detect malicious behavior to provide …

With the rapid growth in network-based applications, new risks arise, and different security
mechanisms need additional attention to improve speed and accuracy. Although many new …

The enormous increase in the use of the Internet in daily life has provided an opportunity for
the intruder attempt to compromise the security principles of availability, confidentiality, and …

Machine learning (ML) has been frequently used to build intelligent systems in many
problem domains, including cybersecurity. For malicious network activity detection, ML …