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

As the world moves towards being increasingly dependent on computers and automation,
building secure applications, systems and networks are some of the main challenges faced …

The increasing popularity of the Internet of Things (IoT) has significantly impacted our daily
lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for …

Detecting a large number of attack classes accurately applying machine learning (ML) and
deep learning (DL) techniques depends on the number of representative samples available …

Presently, we are living in a hyper-connected world where millions of heterogeneous
devices are continuously sharing information in different application contexts for wellness …

In contrast to previous surveys, the present work is not focused on reviewing the datasets
used in the network security field. The fact is that many of the available public labeled …

The lack of publicly available up-to-date datasets contributes to the difficulty in evaluating
intrusion detection systems. This paper introduces HIKARI-2021, a dataset that contains …

This article presents a cloud-based method to classify 0-day attacks from a novel dataset
called UGRansome1819. The primary objective of the research is to classify potential …

Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD)
in the recent years. A substantial research effort has been invested in the development of …

This research attempts to introduce the production methodology of an anomaly detection
dataset using ten desirable requirements. Subsequently, the article presents the produced …