The Knowledge Extraction Analytics (KEA) tool identifies a set of effective techniques that harvest the knowledge that is extracted from health data sources or network monitoring, in order to reveal vulnerabilities and the profile of threats that appear and happen in health systems. KEA adopts methodologies in order to predict future incidents and identify new threat patterns. More specifically, KEA applies both misuse and anomaly detection techniques. It is important to highlight that KEA follows a hybrid detection approach through advocating ensemble techniques comprising multiple complementary algorithmic solutions for both misuse and anomaly detection.
|Deliverable||D3.2 Knowledge Extraction Analytics (November 2019)|
|Paper||Bellas, Christos, Athanasios Naskos, Georgia Kougka, George Vlahavas, Anastasios Gounaris, Athena Vakali, Apostolos Papadopoulos, Evmorfia Biliri, Nefeli Bountouni, and Gustavo Gonzalez Granadillo. "A Methodology for Runtime Detection and Extraction of Threat Patterns." SN Computer Science 1 (2020): 1-13.|
|Asset page||KEA on the Horizon Results Platform|