A Methodology for Runtime Detection and Extraction of Threat Patterns

Abstract

As the confidentiality and integrity of modern health infrastructures is threatened by intrusions and real-time attacks related to privacy and cyber-security, there is a need for proposing novel methodologies to predict future incidents and identify new threat patterns. The main scope of this article is to propose an advanced extension to current Intrusion Detection System (IDS) solutions, which (i) harvests the knowledge out of health data sources or network monitoring to construct models for new threat patterns and (ii) encompasses methods for detecting threat patterns utilizing also advanced unsupervised machine learning data analytic methodologies. Although the work is motivated by the health sector, it is developed in a manner that is directly applicable to other domains.

A Trusted Platform Module-based, Pre-emptive and Dynamic Asset Discovery Tool

“…Moreover, the information discovered by ISADT can be used by other tools and methodologies, like the one presented in [21], to predict future incidents and identify new threat patterns in each single asset. This information can also be used to detect the cyber-security and the privacy risks in a device before an information exchange is done, as presented in [22].…”

Cybersecurity and Privacy Risk Assessment of Point-of-Care Systems in Healthcare—A Use Case Approach

“…Upon reception of the vulnerability report, KEA performs a machine learning analysis using the log events generated in the infrastructure in order to detect new threat patterns that could potentially harm the system [24]. New threat patterns are added to the VDM, which enriches the vulnerability report to be shared with other CUREX tools.…”