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Application of artificial intelligence methods in the security audit of IT systems

Leading article: 2021. December (MIAU No. 280.)
(Previous article: MIAU No. 279.)

Keywords: genetic potential, similarity analyses, modelling without classic testing

Abstract: C1: Following the Knuth principle, in order to increase the efficiency of information security audits, a decision support system (robot auditor) should be created with artificial intelligence, which can automatically explore the interrelations between control gaps and control areas and make suggestions for detection of potential human error to reduce detection risk. H1: Creating a structured database from information security audit reports and processing it as input into the decision support system, the existence of control deficiencies to be detected during audits is more likely to be detected than random guessing.  H1.1: Evaluating the specifics of suspicion generation as a businessinterpreted problem to be solved, the detection of control deficiencies can be solved by both supervised and unsupervised machine learning procedures.  H1.2: The performance of suspicion generation can be enhanced in a hybrid approach, i.e. the relevant performance metrics used in the research of the combined use of supervised and unsupervised methods show more ideal values than in stand-alone application.  H1.3: The hybrid model is able to improve the generalization power of simple models. C2: The software robot auditor with artificial intelligence to be developed, must be able to use the available data set as optimally as possible, thereby maximizing its performance, i.e. the goal is to exploit the genetic potential of the robot auditor through controlled reduction of the learning data set. H2: The genetic potential of a decision support system can be reached by a search procedure with similarity analysis through controlled processing of the data set used for training, so that the search procedure leading to genetic potential can provide a more ideal result without random mutation and population crossings. C3: In order to increase the information value of the data set used for machine learning, it is necessary to compare the performance of each robot auditor alternative in an anti-discriminatory way, to select the best alternative, which does not require separation of validation and test set. H3: Artificial intelligence decision support systems can be ranked on a performance basis without classical testing procedures for machine learning applications, deriving the value direction of the descriptive properties of predictions as generated sources of suspicion and the mathematical apparatus processing this data automatically to objectively determine preferred models. (PDF_HU) *** (PDF_EN)


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