Fuzzy logic for pipelines risk assessment

Alidoosti, Ali; Jamshidi, Ali Ashraf; Yakhchali, Siamak Haji; Basiri, Mohammad Hossein; Azizi, Ramin; Yazdani–Chamzini, Abdolreza

doi:10.5267/j.msl.2012.04.017

Cited by 8 publications

(5 citation statements)

References 26 publications

(27 reference statements)

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“…In this section, we compare the results of our approach with other study results from the literature to generalise our findings. In [38], fuzzy risk analysis and management for critical asset protection (RAMCAP) is introduced in order to risk analysis and management for pipeline systems. However, the fuzzy RAMCAP considers the relative importance among vulnerability, threat, and consequence but not the relevant goals for the assets.…”

Section: Comparison With the Other Study Resultsmentioning

confidence: 99%

Asset criticality and risk prediction for an effective cybersecurity risk management of cyber-physical system

Kure

Islam

Ghazanfar

et al. 2021

Neural Comput & Applic

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Risk management plays a vital role in tackling cyber threats within the cyber-physical system (CPS). It enables identifying critical assets, vulnerabilities and threats and determining suitable proactive control measures for the risk mitigation. However, due to the increased complexity of the CPS, cyber-attacks nowadays are more sophisticated and less predictable, which makes risk management task more challenging. This paper aims for an effective cybersecurity risk management (CSRM) practice using assets criticality, predication of risk types and evaluating the effectiveness of existing controls. We follow a number of techniques for the proposed unified approach including fuzzy set theory for the asset criticality, machine learning classifiers for the risk predication and comprehensive assessment model (CAM) for evaluating the effectiveness of the existing controls. The proposed approach considers relevant CSRM concepts such as asset, threat actor, attack pattern, tactic, technique and procedure (TTP), and controls and maps these concepts with the VERIS community dataset (VCDB) features for the risk predication. The experimental results reveal that using the fuzzy set theory in assessing assets criticality supports stakeholder for an effective risk management practice. Furthermore, the results have demonstrated the machine learning classifiers exemplary performance to predict different risk types including denial of service, cyber espionage and crimeware. An accurate prediction of risk can help organisations to determine the suitable controls in proactive manner to manage the risk.

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Section: Comparison With the Other Study Resultsmentioning

confidence: 99%

Asset criticality and risk prediction for an effective cybersecurity risk management of cyber-physical system

Kure

Islam

Ghazanfar

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Wulan and Petrovic ( 2012 ) present a fuzzy logic-based system for risk analysis and assessment within their corporate collaboration. Alidoosti et al ( 2012 ) developed a method to analyze critical risk elements in pipeline systems by using fuzzy logic. Shi et al ( 2014 ) examined the analysis of delivery methods on how to reduce the risk of a construction program with the help of fuzzy sets.…”

Section: Literature Reviewmentioning

confidence: 99%

Action selection in risk assessment with fuzzy Fine–Kinney-based AHP-TOPSIS approach: a case study in gas plant

Dogan

Oturakci

Dağsuyu

2022

Environ Sci Pollut Res

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In this study, the hazards occurring in a medium-sized gas filling facility were defined, and the risk scores of these hazards were determined by the expert team according to the Fine–Kinney risk analysis method. However, since the same risk significance score is obtained in different combinations of scale values in the classical Fine–Kinney risk analysis method and the characteristics/constraints of the company applied in the risk analysis are not taken into account, the hazards were evaluated using fuzzy Fine–Kinney risk analysis, and the most critical hazards were determined. Action plans are defined for critical hazards determined as a result of fuzzy Fine–Kinney risk analysis. Among the actions that require company resources, the action selection was performed with the TOPSIS method, taking into account their relationship with the hazards by integrating the weights, which was calculated with the AHP method, of affected groups. The effect of operating constraints is included in the last step of the study to calculate the final weights. Calculating the results by including the risk-affected groups and company constraints and ranking the actions reveals that the study is an original, objective, and applicable study.

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“…The fuzzy inference system (FIS) is defined as receiving output based on the input through the use of fuzzy logic (Alidoosti, et al, 2012). An essential advantage of FIS is its ability to use linguistic terms to provide an inference framework for modeling complex problems.…”

Section: Fuzzy Inference System (Fis)mentioning

confidence: 99%

Establishing the role of BIM towards mitigating critical project risks assessed using a fuzzy inference system

Gaur¹,

Tawalare²

2022

Proceedings of 10th World Construction Symposium 2022

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Risk management is an essential process for the successful execution of the project, and it is pertinent in achieving the project objectives and leading to its successful outcome. The nature of the construction industry, which is full of uncertainty and high capital investment, makes it notably more critical to address and manage the risks promptly. The most important part of the risk management process is identifying and assessing risks. However, the traditional Probability (P)-Impact (I) matrix used in their evaluation fails to account for the uncertainties witnessed in the determination of both P and I. This paper, therefore, uses a fuzzy approach to develop a risk assessment model. Further, the results of the generated model are compared with the conventional P-I matrix to show the effectiveness of the adopted fuzzy system. The data for the model development was collected from one of the metro-rail projects through a questionnaire. Subsequently, semi-structured interviews were conducted to identify the advantages of BIM in the project. The recognized BIM advantages were then correlated with the critical project risks to present it as the process for mitigating these risks. The study findings present the use of FIS to overcome the uncertainty in the risk management process, followed by the applicability of BIM as a risk mitigation tool. Establishing the role of BIM in the risk mitigation process can help in its wider acceptance in the construction industry.

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Fuzzy logic for pipelines risk assessment

Cited by 8 publications

References 26 publications

Asset criticality and risk prediction for an effective cybersecurity risk management of cyber-physical system

Asset criticality and risk prediction for an effective cybersecurity risk management of cyber-physical system

Action selection in risk assessment with fuzzy Fine–Kinney-based AHP-TOPSIS approach: a case study in gas plant

Establishing the role of BIM towards mitigating critical project risks assessed using a fuzzy inference system

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