This research introduces a theoretical framework for network anomaly detection in
cybersecurity, emphasizing the integration of adaptive machine learning models, ensemble …

Deep neural networks (DNNs) have been widely employed to solve log anomaly detection
and outperform a range of conventional methods. They have attained such striking success …

Nowadays, systems or entities are usually monitored by devices, generating large amounts
of time series. Detecting anomalies in them help prevent potential losses, thus arousing …

Cybersecurity is an arms race, with both the security and the adversaries attempting to
outsmart one another, coming up with new attacks, new ways to defend against those …

The number of security breaches in the cyberspace is on the rise. This threat is met with
intensive work in the intrusion detection research community. To keep the defensive …

Network Intrusion Detection Systems are an important part of cyber-defensive inventory.
Currently, Machine-Learning-Based Network Intrusion Detection Systems are being …

As automobiles become more electrified, more and more Electronic Control Units (ECU) are
installed in vehicles. ECUs communicate with each other through dedicated protocols such …

The rapid advancement of Artificial Intelligence in the field of cybersecurity brings about both
opportunity and vulnerability, like a dual-edged sword. The research community expressed …

Research to deal with distributed denial of service (DDoS) attacks was kicked off from long
ago and has seen technological advancement along with an extensive 5G footprint. Prior …

Since the invention of the Blockchain technology in 2008, it has been used in many domains
to ensure high security and reliability of data, like from the use of Bitcoin to BaaS (Blockchain …