Previous studies have adopted unsupervised machine learning with dimension reduction
functions for cyberattack detection, which are limited to performing robust anomaly detection …

Internet of Things (IoT) fosters unprecedented network heterogeneity and dynamicity, thus
increasing the variety and the amount of related vulnerabilities. Hence, traditional security …

Nowadays intrusion detection systems are a mandatory weapon in the war against the ever-
increasing amount of network cyber attacks. In this study we illustrate a new intrusion …

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 …

Abstract The Internet of Things (IoT) is a key enabler in closing the loop in Cyber-Physical
Systems, providing “smartness” and thus additional value to each monitored/controlled …

The construction of effective health indicators plays a key role in the engineering systems
field: they reflect the degradation degree of the system under study, thus providing vital …

The recent boom in deep learning has revealed that the application of deep neural networks
is a valuable way to address network intrusion detection problems. This paper presents a …

Networks had an increasing impact on modern life since network cybersecurity has become
an important research field. Several machine learning techniques have been developed to …

Anomaly detection is a crucial task in the engineering systems field. However, there is
usually little or no information about all possible abnormal modes in systems. Hence, a …

The problem of network attacks is a primary focus in the domain of intrusion detection.
Models face significant challenges in recognizing intrusion behaviors, particularly when …