A Methodology for Runtime Detection and Extraction of Threat Patterns
Christos Bellas, Athanasios Naskos, Georgia Kougka, George Vlahavas, Anastasios Gounaris, Athena I. Vakali, Apostolos N. Papadopoulos, Evmorfia Biliri, Nefeli Bountouni, Gustavo Gonzalez Granadillo · SN Computer Science · 2020
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.