METHOD FOR DETECTING ANOMALOUS STATES OF A CONTROL OBJECT IN INFORMATION SYSTEMS BASED ON THE ANALYSIS OF TEMPORAL DATA AND KNOWLEDGE

  • Oksana Chala Kharkiv National University of Radio Electronics
Keywords: anomalies, temporal rule, temporal knowledge base, management information system, event attributes

Abstract

The problem of finding the anomalous states of the control object in the management information system under conditions of uncertainty caused by the incompleteness of knowledge about this object is considered. The method of classifying the current state of the control object in real time, allowing to identify the current anomalous state. The method uses temporal data and knowledge. Data is represented by sequences of events with timestamps. Knowledge is represented as weighted temporal rules and constraints. The method includes the following key phases: the formation of sequences of logical facts; selection of temporal rules and constraints; classification based on a comparison of rules and constraints. Logical facts are represented as predicates on event attributes and reflect the state of the control object. Logical rules define valid sequences of logical facts. Performing a classification by successive comparisons of constraints and weights of the rules makes it possible to more effectively identify the anomalous state since the comparison of the constraints reduces the subset of facts comparing to the current state. The method creates conditions for improving management efficiency in the context of incomplete information on the state of a complex object by using logical inference in knowledge bases for anomalous states of such control objects.

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Author Biography

Oksana Chala, Kharkiv National University of Radio Electronics

Department of Information Control Systems

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Published
2018-11-30
How to Cite
Chala, O. (2018). METHOD FOR DETECTING ANOMALOUS STATES OF A CONTROL OBJECT IN INFORMATION SYSTEMS BASED ON THE ANALYSIS OF TEMPORAL DATA AND KNOWLEDGE. EUREKA: Physics and Engineering, (6), 28-35. https://doi.org/10.21303/2461-4262.2018.00787
Section
Computer Science