Insider threat has been recognized by both scientific community and security professionals
as one of the gravest security hazards for private companies, institutions, and governmental …

Anomaly detection generally involves the extraction of features from entities' or users'
properties, and the design of anomaly detection models using machine learning or deep …

Insiders have the potential to do a great deal of damage, given their legitimate access to
organisational assets and the trust they enjoy. Organisations can only mitigate insider …

Cybersecurity incidents do represent a serious danger for companies. In fact, the number of
cyber crimes is exponentially growing in a scenario where the global COVID-19 pandemic …

Insider threat is a persistent concern for organisations and business alike that has attracted
the interest of the research community, resulting in numerous behavioural models and tools …

The exploitation of so-called insiders is increasingly recognised as a common vector for
cyberattacks. Emerging work in this area has considered the phenomenon from various …

Insider threat protection has received increasing attention in the last ten years due to the
serious consequences of malicious insider threats. Moreover, data leaks and the sale of …

We demonstrate that our success in solving a set of increasingly complex challenge
problems is associated with an inference enterprise (IE) using inference enterprise models …

This study proposes a scheme to identify insider threats in nuclear facilities through the
detection of malicious intentions of potential insiders using subject-wise classification …

This paper presents PSO, an ontological framework and a methodology for improving
physical security and insider threat detection. PSO can facilitate forensic data analysis and …