Security Risk Assessment-based Cloud Migration Methodology for Smart Grid OT Services

Jelacic, Bojan; Lendak, Imre; Stoja, Sebastijan; Stanojevic, Marina; Rosic, Daniela

doi:10.12700/aph.17.5.2020.5.6

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…In order to balance the respective advantages and defects of AHP and entropy method and get more objective index weights, it is a better weighting method to combine the empirical advantages of AHP and objective advantages of entropy method by means of comprehensive weighting [26][27][28]. Comprehensive weighting method is to combine the weighting results of analytic hierarchy process and entropy value method.…”

Section: Determination Methods Of Index Weightmentioning

confidence: 99%

[Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm

Chen

2021

Complexity

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Government debt risk is an important factor affecting macroeconomic stability and public expectation. The key to its prevention and control lies in early warning and early prevention. This paper builds an effective government debt risk assessment system based on machine learning algorithm. According to forming the performance of local government debt risk and its internal and external influencing factors, this study applies the analytic hierarchy process, entropy method, and BP neural network method to construct the local government risk assessment index system, which includes the primary and secondary indexes including the explicit debt risk, the contingent implicit debt risk, and the financial and economic operation risk. Using this system, this study carries on the government debt risk comprehensive weight assignment, the fiscal revenue forecast, the default probability calculation, the safety scale forecast, and finally the government debt risk assessment of the validity analysis. The system can provide signal guidance and policy reference for finance to cope with risks in advance, arrange the priority order of debt repayment, optimize the structure of fiscal revenue and expenditure, etc.

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Section: Determination Methods Of Index Weightmentioning

confidence: 99%

[Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm

Chen

2021

Complexity

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“…The IoT-Hub provides the opportunity to implement a real-time data processing, which is called Stream Analytics (the hot route of data processing). This analysis mode can provide results describing the current state of the system and useable information can be obtained based on the results of the analyses [17], [19].…”

Section: Security Issues Of Iiot Systemsmentioning

confidence: 99%

Cloud Integration of Industrial IoT Systems. Architecture, Security Aspects and Sample Implementations

Ferencz,

Domokos,

Kovács

2024

ACTA POLYTECH HUNG

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Today's industry is increasingly characterized by the integration of Internet of Things (IoT) devices and the rapidly spreading digitization trend, which are also known as the foundations of Industry 4.0. The industrial revolution characterizes our everyday life, as a result of which the integration of smart devices is gaining more and more space in industry as well. As a result of the introduction of Industry 4.0, the implementation of the automation of production processes also becomes an important issue, as well as the continuous collection of data and their storage in the cloud. As a result of these integration, the method of collecting data, the usability of cloud-based systems, the feasibility of artificial intelligence-based data analysis, the visualization of massive amounts of collected data, cybersecurity issues, etc. have become everyday issues. In this article, we provide a detailed overview of a general IIoT (Industrial IoT) system, its various cloud service-based implementations, and present our open-source alternative solution. At the same time, we present the study of the security aspects and the integration of SOC into an IIoT system. Taking these aspects into account, we present two sample applications that try to offer ready to use solutions to certain problems of today's industry.

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“…This means that the perpetrator estimates the differences between competencies to be significant, so interventions can be repeated several times without the risk of being discovered. Attacks carried out as continuous intervention, are often implemented to influence strongly safety critical systems by moving system state from safe and secured operational circumstances to a high risk operational domain [39]. These kind of incidents can be implemented for example by denial-of-service attacks in which the attacker aims to make a system resource ineffectual to the regular user for instance by flooding it from different sources [40].…”

Section: Figure 7 Conceptualization Framework Of Cybersecurity Relatementioning

confidence: 99%

Reconsidering the Cybersecurity Framework in the Road Transportation Domain

Obaid¹,

Szalay²,

Török³

2020

ACTA POLYTECH HUNG

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The Automotive Industry has advanced significantly and this process is constantly continuing. Cars are controlled by hundreds of electrical units, which affects directly numberless safety and security related risk factors, especially considering incidents realized in the cyberspace. Due to the increasing number of connected cars, new testing facilities and methods need to be established, to support the solutions, for these novel challenges. For this reason, the Hungarian government has founded the Zalaegerszeg test track, and its special purpose is to provide space for the validation of future vehicles. The overview focused on the presently applied validation methods and processes has highlighted, that cybersecurity related testing methodsfrom a scientific point of vieware only slightly detailed in the sector of Automobile Industry. In a sector specific term, the lack of a holistic approach, may result a relevant barrier, in the future, for providing a socially tolerable level of cybersecurity. Accordingly, this paper aims to introduce a comprehensive cybersecurity reference model, to provide a solid basis for describing attack patterns and characterizing malicious intervention profiles.

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Security Risk Assessment-based Cloud Migration Methodology for Smart Grid OT Services

Cited by 10 publications

References 16 publications

[Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm

[Retracted] Risk Assessment of Government Debt Based on Machine Learning Algorithm

Cloud Integration of Industrial IoT Systems. Architecture, Security Aspects and Sample Implementations

Reconsidering the Cybersecurity Framework in the Road Transportation Domain

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