Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …

Abstract The Internet of Things (IoT) envisions a smart environment powered by connectivity
and heterogeneity where ensuring reliable services and communications across multiple …

With the booming of cyber attacks and cyber criminals against cyber-physical systems
(CPSs), detecting these attacks remains challenging. It might be the worst of times, but it …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

Monitoring the “physics” of cyber-physical systems to detect attacks is a growing area of
research. In its basic form, a security monitor creates time-series models of sensor readings …

As one of the key components of the smart grid, advanced metering infrastructure brings
many potential advantages such as load management and demand response. However …

Despite decades of research in network traffic analysis and incredible advances in artificial
intelligence, network intrusion detection systems based on machine learning (ML) have yet …

Large enterprises are increasingly relying on threat detection softwares (eg, Intrusion
Detection Systems) to allow them to spot suspicious activities. These softwares generate …

While attacks on information systems have for most practical purposes binary outcomes
(information was manipulated/eavesdropped, or not), attacks manipulating the sensor or …

Network anomaly detection is an important and dynamic research area. Many network
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …