Due to the frequency of malicious network activities and network policy violations, intrusion
detection systems (IDSs) have emerged as a group of methods that combats the …

Network security plays an essential role in secure communication and avoids financial loss
and crippled services due to network intrusions. Intruders generally exploit the flaws of …

The popularity of using Internet contains some risks of network attacks. Intrusion detection is
one major research problem in network security, whose aim is to identify unusual access or …

Due to the extensive use of computer networks, new risks have arisen, and improving the
speed and accuracy of security mechanisms has become a critical need. Although new …

One of the major developments in machine learning in the past decade is the ensemble
method, which finds highly accurate classifier by combining many moderately accurate …

Background: Building an effective Intrusion detection system in a multi-attack classification
environment is challenging due to the diversity of modern, sophisticated attacks. High …

The growth of Internet and the services provided by it has been growing exponentially in the
past few decades. With such growth, there is also an ever-increasing threat to the security of …

Several studies have used machine learning algorithms to develop intrusion systems (IDS),
which differentiate anomalous behaviours from the normal activities of network systems. Due …

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 …

An efficient machine learning (ML) ensemble technique for categorizing Intrusion Detection
(ID) is proposed in this study. The tuning of the ML model's parameters is a critical topic …