Various machine learning algorithms have been applied to network intrusion classification
problems, including both binary and multi-class classifications. Despite the existence of …

The security of networked systems has become a critical universal issue that influences
individuals, enterprises and governments. The rate of attacks against networked systems …

Early and effective network intrusion detection is deemed to be a critical basis for
cybersecurity domain. In the past decade, although a significant amount of work has focused …

The rapid development of information technology has brought numerous benefits to society,
but it has also led to increased security vulnerabilities in network systems. Intrusion …

This paper is devoted to the problem of class imbalance in machine learning, focusing on
the intrusion detection of rare classes in computer networks. The problem of class imbalance …

Balanced datasets play a key role in the bias observed in machine learning algorithms
towards classification and prediction. The CSE-CIC IDS datasets published in 2017 and …

Class imbalance is a common issue in real-world machine learning datasets. This problem
is more obvious in intrusion detection since many attack types only have very few samples …

In machine learning, balanced datasets play a crucial role in the bias observed towards
classification and prediction. The CSE-CIC IDS datasets published in 2017 and 2018 have …

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

All organizations, be they businesses, governments, infrastructure or utility providers,
depend on the availability and functioning of their computers, computer networks and data …