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Vol. 2 No. 1 (2012): JITA - APEIRON

Nasim Beigi-Mohammadi, Hamzeh Khazaei, Jelena Mišić, Vojislav B. Mišić

On Intrusion Detection in a Neighbourhood Area Network in the Smart Grid

Original scientific paper

DOI: https://doi.org/10.7251/JIT1201007M

Abstract

Smart grid, which is an upgrade of power electric system, mainly relies on powerful communication networks to provide a secure, reliable and efficient information delivery. Updating a system as complex as the electrical power grid with a large number of components has the potential of introducing new security vulnerabilities into the system. Hence, security mechanisms should be deployed to protect the smart grid as a first wall of defence against malicious attacks. As a second wall of defence, there should be intrusion detection systems in place to protect the smart grid against any security breaches. In this work, we describe an anomaly-based intrusion detection system (IDS) for neighbourhood area network whose security is of critical importance in smart grid.

Keywords: Intrusion Detection System, smart grid, Neighbourhood Area Network

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.