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Title: Dynamic Expert System-Based Geographically Adapted Malware Risk Evaluation Method
Authors: Vitkus, Donatas
Jezukevičiūtė, Justina
Goranin, Nikolaj
Keywords: information security risk analysis
expert systems
knowledge base formation
JESS
information acquisition
Issue Date: 2020
Publisher: Agora University Press
Citation: Vitkus, D., Jezukevičiūtė, J., & Goranin, N. (2020). Dynamic Expert System-Based Geographically Adapted Malware Risk Evaluation Method. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 15(3). doi:10.15837/ijccc.2020.3.3865
Series/Report no.: 15;3
Abstract: Fast development of information systems and technologies while providing new opportunities for people and organizations also make them more vulnerable at the same time. Information security risk assessment helps to identify weak points and preparing mitigation actions. The analysis of expert systems has shown that rule-based expert systems are universal, and because of that can be considered as a proper solution for the task of risk assessment automation. But to assess information security risks quickly and accurately, it is necessary to process a large amount of data about newly discovered vulnerabilities or threats, to reflect regional and industry specific information, making the traditional approach of knowledge base formation for expert system problematic. This work presents a novel method for an automated expert systems knowledge base formation based on the integration of data on regional malware distribution from Cyberthreat real-time map providing current information on newly discovered threats. In our work we collect the necessary information from the web sites in an automated way, that can be later used in a relevant risk calculation. This paper presents method implementation, which includes not only knowledge base formation but also the development of the prototype of an expert system. It was created using the JESS expert system shell. Information security risk evaluation was performed according to OWASP risk assessment methodology, taking into account the location of the organization and prevalent malware in that area.
Description: This is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International License.
URI: http://dspace.vgtu.lt/handle/1/4179
ISSN: 1841-9844
1841-9836 ISSN-L
Appears in Collections:Moksliniai straipsniai / Research articles

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