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Vol. 12 No. 2 (2022): JITA - APEIRON

Mikhalevich Igor Feodosevich, Fedorenko Bogdan Nikolaevich, Shelamov Maxim Dmitrievich

Research of Vulnerability Scanners of Web Applications of Intelligent Transport Systems

Original scientific paper

DOI:https://doi.org/10.7251/JIT2202127F

Abstract

The intellectualization of transport systems is accompanied by the widespread use of web applications. The paper presents a system of criteria for evaluating the effectiveness of vulnerability scanners for web applications of intelligent transport systems, the features of the functioning of which impose additional requirements for the secure development of applications used in critical information infrastructure and systems interacting with it. A study was made of the most famous web application vulnerability scanners.

Keywords: Attack, computer attack, critical information infrastructure, information security, information security threat, intelligent transport system, vulnerability scanner, vulnerability web application.

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.