In a computing context, cybersecurity is undergoing massive shifts in technology and its
operations in recent days, and data science is driving the change. Extracting security …

Over the past decades, researchers have been proposing different Intrusion Detection
approaches to deal with the increasing number and complexity of threats for computer …

Learning temporal patterns in time series remains a challenging task up until today.
Particularly for anomaly detection in time series, it is essential to learn the underlying …

The rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …

Threats are potential events, intentional or not, that compromise the confidentiality, integrity,
and/or availability of information systems. Defending against threats and attacks requires …

Owing to the swift growth in cyber attacks, intrusion detection systems (IDSs) have become a
necessity to help safeguard personal and organizational assets. However, with the …

This is a systematic review of over one hundred research papers about machine learning
methods applied to defensive and offensive cybersecurity. In contrast to previous reviews …

The rapid increase of diverse Internet of Things (IoT) services and devices has raised
numerous challenges in terms of connectivity, interoperability, and security. The …

Abstract Host-based Intrusion Detection System (HIDS) is an effective last line of defense for
defending against cyber security attacks after perimeter defenses (eg, Network-based …

Cybersecurity continues to be one of the principal issues in the computing environment.
Organizations and researchers have made various efforts to mitigate the risks of cyberspace …