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Vol. 3 No. 1 (2013): JITA - APEIRON

Ivan Milenković, Olja Latinović, Dejan Simić

Using Kerberos protocol for Single Sign-On in Identity Management Systems

Original scientific paper

DOI: https://doi.org/10.7251/JIT1301020E

Abstract

Today, identity management systems are widely used in different types of organizations, from academic and government institutions to large enterprises. An important feature of identity management systems is the Single Sign-On functionality. Single Sign-On allows users to authenticate once, and freely use all services and resources available to them afterwards. In this paper, we present the usage of Kerberos in identity management systems. An overview of Kerberos protocol, state of the art of identity management systems and different generic architectures for identity management is given in the paper. Also, we present a Single Sign-On identity management architecture proposal based on Kerberos protocol, and discuss its properties. Special attention was given to authentication, authorization and auditing.

Keywords: identity management, authentication, Kerberos, Single Sign-On.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.