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The aim of this study is to develop a fuzzy MCDM based framework for security management in smart cities. The study focuses on the assessment of security risks and the selection of appropriate security measures. To address the uncertainties and complexities in the assessment of security risks and measures, this research employs two different fuzzy methods: weighting the risk factors using the Fuzzy weight by envelope and slope (F-WENSLO) method and selecting the most appropriate security measures using the Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) method. The proposed approach is applied to a case study on identifying and assessing potential security risks in a smart city infrastructure. The risk factors used in the study are identified as flash floods, traffic accidents and transportation security, robbery and public safety threats, cybersecurity threats, health crises and pandemics, and energy infrastructure and outages. Security systems are defined as physical security systems, emergency response and crisis management systems, software and cybersecurity systems, monitoring and surveillance systems, and sensor-based early warning systems. The results show that physical security measures play a critical role in preventing and responding to incidents, while emergency response and crisis management systems perform less well. It is also stated that software and cybersecurity systems provide an effective solution against digital threats, but their integration with other systems is important. The results show that the fuzzy-based model significantly increases the accuracy in prioritizing risks and provides a systematic method for selecting the most effective security measures. This study contributes to the literature by presenting an innovative decision-making tool for security management in smart cities.
Gülay Demir (Sun,) studied this question.