Machine Learning is widely used in cybersecurity for detecting network intrusions. Though
network attacks are increasing steadily, the percentage of such attacks to actual network …

This study, focusing on identifying rare attacks in imbalanced network intrusion datasets,
explored the effect of using different ratios of oversampled to undersampled data for binary …

Abstract Machine learning plays an increasingly significant role in the building of Network
Intrusion Detection Systems. However, machine learning models trained with imbalanced …

Abstract Machine learning methods are now widely used to detect a wide range of
cyberattacks. Nevertheless, the commonly used algorithms come with challenges of their …

Research on the application of machine learning to the field of intrusion detection is
attracting great interest. However, depending on the application, it is difficult to collect the …

Cyber-security experts often require the help of an automated process that filters and
classifies network attacks. To apply specific preventive measures for securing networks, the …

Network intrusion detection system (NIDS) is the most used tool to detect malicious network
activities. The NIDS has achieved in the recent years promising results for detecting known …

Network intrusion detection research using KDDCUP 99 dataset often encounters
challenges that classifiers could not handle the problem of uneven distribution of attack …

Unbalanced datasets are a common problem in supervised machine learning. It leads to a
deeper understanding of the majority of classes in machine learning. Therefore, the machine …

Despite the enormous amount of data, particular events of interest can still be quite rare.
Classification of rare events is a common problem in many domains, such as fraudulent …