ABSTRACT
Information on cyber-related crimes, incidents, and conflicts is abundantly available in numerous open online sources. However, processing large volumes and streams of data is a challenging task for the analysts and experts, and entails the need for newer methods and techniques. In this article we present and implement a novel knowledge graph and knowledge mining framework for extracting the relevant information from free-form text about incidents in the cyber domain. The computational framework includes a machine learning-based pipeline for generating graphs of organizations, countries, industries, products and attackers with a non-technical cyber-ontology. The extracted knowledge graph is utilized to estimate the incidence of cyberattacks within a given graph configuration. We use publicly available collections of real cyber-incident reports to test the efficacy of our methods. The knowledge extraction is found to be sufficiently accurate, and the graph-based threat estimation demonstrates a level of correlation with the actual records of attacks. In practical use, an analyst utilizing the presented framework can infer additional information from the current cyber-landscape in terms of the risk to various entities and its propagation between industries and countries.
ABSTRACT
The purpose of this study is to determine the applicability of Radio Frequency Identification (RFID) technology and commercial cellular networks to provide an online triage system for handling mass casualty situations. This was tested by a using a pilot system for a simulated mass casualty situation during a military field exercise. The system proved to be usable. Compared to the currently used system, it also dramatically improves the general view of mass casualty situations and enhances medical emergency readiness in a military medical setting. The system can also be adapted without any difficulties by the civilian sector for the management of mass casualty disasters.
