We present a detailed survey of the Zero Trust (ZT) security paradigm which has a growing
number of advocates in the critical infrastructure risk management space. The article …

Anomaly detection is an active area of research with numerous methods and applications.
This survey reviews the state-of-the-art of data-driven anomaly detection techniques and …

Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

The use of deep learning models for the network intrusion detection task has been an active
area of research in cybersecurity. Although several excellent surveys cover the growing …

Detecting anomalous events in online computer systems is crucial to protect the systems
from malicious attacks or malfunctions. System logs, which record detailed information of …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

The massive growth of data that are transmitted through a variety of devices and
communication protocols have raised serious security concerns, which have increased the …

There has been significant research in incorporating both blockchain and intrusion detection
to improve data privacy and detect existing and emerging cyberattacks, respectively. In …

Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …

Nowadays attacks on computer networks continue to advance at a rate outpacing cyber
defenders' ability to write new attack signatures. This paper illustrates a deep learning …