There is an astounding growth in the adoption of machine learners (MLs) to craft intrusion
detection systems (IDSs). These IDSs model the behavior of a target system during a …

In the last decade, researchers, practitioners and companies struggled for devising
mechanisms to detect cyber-security threats. Among others, those efforts originated rule …

Abstract The application of Deep Neural Networks (DNNs) to a broad variety of tasks
demands methods for coping with the complex and opaque nature of these architectures …

Anomaly detection aims at identifying unexpected fluctuations in the expected behavior of a
given system. It is acknowledged as a reliable answer to the identification of zero-day …

Artificial Intelligence (AI)-based classifiers rely on Machine Learning (ML) algorithms to
provide functionalities that system architects are often willing to integrate into critical Cyber …

In the last decades, researchers, practitioners and companies struggled in devising
mechanisms to detect malicious activities originating security threats. Amongst the many …

Anomaly detection aims at finding patterns in data that do not conform to the expected
behavior. It is largely adopted in intrusion detection systems, relying on unsupervised …

Cryptojacking, the appropriation of users' computational resources without their knowledge
or consent to obtain cryp-tocurrencies, is a widespread attack, relatively easy to implement …

Anomaly detection aims at identifying patterns in data that do not conform to the expected
behavior, relying on machine-learning algorithms that are suited for binary classification. It …

Manufacturers are willing to incorporate Machine Learning (ML) algorithms into their
systems, especially those considered as Safety-Critical Systems (SCS). ML algorithms that …