Knowledge mining of unstructured information: application to cyber domain
Loading...
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
Date
2023-12
Department
Department of Computer Science
Department of Industrial Engineering and Management
University of Jyväskylä
Cyberwatch Finland
Kaski Kimmo group
Department of Computer Science
Department of Industrial Engineering and Management
Department of Industrial Engineering and Management
University of Jyväskylä
Cyberwatch Finland
Kaski Kimmo group
Department of Computer Science
Department of Industrial Engineering and Management
Major/Subject
Mcode
Degree programme
Language
en
Pages
13
1-13
1-13
Series
Scientific Reports, Volume 13, issue 1
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.Description
Funding Information: TT, KB, ML and KK acknowledge research project funding from Cyberwatch Finland. AC is the CEO of the company. PJ and AC are founders and partners in the company. KK and ML are on the advisory board of the company. Funding Information: TT, KB, ML and KK acknowledge research project funding from Cyberwatch Finland. TT acknowledges funding from the Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters. Publisher Copyright: © 2023, The Author(s).
Keywords
Citation
Takko , T , Bhattacharya , K , Lehto , M , Jalasvirta , P , Cederberg , A & Kaski , K 2023 , ' Knowledge mining of unstructured information: application to cyber domain ' , Scientific Reports , vol. 13 , no. 1 , 1714 , pp. 1-13 . https://doi.org/10.1038/s41598-023-28796-6
