Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …

The expeditious growth of the World Wide Web and the rampant flow of network traffic have
resulted in a continuous increase of network security threats. Cyber attackers seek to exploit …

Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning
(ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where …

Abstract Of late, Network Security Research is taking center stage given the vulnerability of
computing ecosystem with networking systems increasingly falling to hackers. On the …

With the development of new technologies such as big data, cloud computing, and the
Internet of Things, network attack technology is constantly evolving and upgrading, and …

The pursuit to spot abnormal behaviors in and out of a network system is what led to a
system known as intrusion detection systems for soft computing besides many researchers …

Network intrusion detection systems (NIDS) are the most common tool used to detect
malicious attacks on a network. They help prevent the ever-increasing different attacks and …

The introduction of deep learning techniques in intrusion detection problems has enabled
an enhanced standard of detection effectiveness. However, most of the progress has …

The widespread use of interconnectivity and interoperability of computing systems have
become an indispensable necessity to enhance our daily activities. Simultaneously, it opens …

Growing number of network devices and services have led to increasing demand for
protective measures as hackers launch attacks to paralyze or steal information from victim …