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Artificial intelligence techniques for information security risk assessment

In computer security audits, information security risk assessments (ISR) are performed to computer systems, within it to database management systems (DBMS), often using qualitative methodologies. In these methodologies, the evaluation of the ISR is classified according to its impact in linguistic te...

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Published in:	Revista IEEE América Latina 2018-03, Vol.16 (3), p.897-901
Main Authors:	Azan Basallo, Yasser, Estrada Senti, Vivian, Martinez Sanchez, Natalia
Format:	Article
Language:	eng ; spa
Subjects:	Artificial intelligence Audits Computer security Cybersecurity Data base management systems Fuzzy logic IEEE transactions Monitoring risk Risk assessment Silicon XML
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description	In computer security audits, information security risk assessments (ISR) are performed to computer systems, within it to database management systems (DBMS), often using qualitative methodologies. In these methodologies, the evaluation of the ISR is classified according to its impact in linguistic terms such as: High, Medium or Low, so that ambiguities can be generated in the evaluation result. Security checklists are also used to review the configurations of the DBMS. They have a strong dependence on the presence of the expert auditor in DBMS for this analysis. In order to facilitate the work of the auditors, a model based on knowledge and fuzzy logic was developed for the evaluation of the ISR in the DBMS. In this way, the experience in previous audits of this type is useful and improves the results in the evaluation of the ISR.
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subjects	Artificial intelligence Audits Computer security Cybersecurity Data base management systems Fuzzy logic IEEE transactions Monitoring risk Risk assessment Silicon XML
title	Artificial intelligence techniques for information security risk assessment
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