This systematic literature review explores the digital transformation (DT) and cybersecurity
implications for achieving business resilience. DT involves transitioning organizational …

Abstract Machine learning (ML) has shown to be an effective alternative to physical models
for quality prediction and process optimization of metal additive manufacturing (AM) …

In this paper, we identify and review key challenges to bridge the knowledge-gap between
SME's, companies, organisations, businesses, government institutions and the general …

Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …

Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly
improved sensing accuracy; however, their vulnerability to adversarial attacks affects their …

The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …

Abstract Adversarial Machine Learning (AML) is a recently introduced technique, aiming to
deceive Machine Learning (ML) models by providing falsified inputs to render those models …

Abstract Machine learning based Intrusion Detection Systems (IDS) allow flexible and
efficient automated detection of cyberattacks in Internet of Things (IoT) networks. However …

Abstract Machine Learning (ML) can be incredibly valuable to automate anomaly detection
and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is …

Abstract Adversarial Machine Learning (AML) discusses the act of attacking and defending
Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …