Defining Cyber Risk Scenarios to Evaluate IoT Systems
Abstract
:1. Introduction
 Level 1: the pervasiveness of technology could disrupt several organizations simultaneously;
 Level 2: interdependencies between organizations, as an organization’s cybersecurity failure presents a potential risk of affecting its networking organizations;
 Level 3: cybersecurity failure, which could be systematically catastrophic to economies and societies. Multiple financial and social sectors could fail.
 Repeated attacks;
 Scattershot attacks;
 Pervasive attacks;
 Rolling attacks;
 Transitive attacks;
 Cascading attacks;
 Shared resource consumption attacks;
 Critical function attacks;
 Regional attacks;
 Service dependency attacks;
 Coordinated supply chain attacks.
 Bayesian networks allow for realtime tracking of how event probabilities change as new evidence is introduced into the model;
 Bayesian networks define how the different network nodes are linked. Additionally, they study how the probabilities change after introducing some evidence into specific nodes;
 Bayesian networks could make predictions under scenarios of limited and uncertain data.
2. Literature Review
2.1. Systematic Literature Review—Bayesian Networks Applied to Cybersecurity
 (i)
 Identification, which is related to evaluating previous studies from scientific databases and searching the use of Bayesian networks for IoT security. The previous studies were explored according to the following keywords: (a) “Security and (Bayes Network or Bayesian Network)”, (b) “Security attacks and (Bayes Network or Bayesian Network)” and (c) “Cybersecurity attacks and (Bayes Network or Bayesian Network)”. The used scientific databases were IEEE Xplorer, Scopus, ACM and Springer. The method search was performed to find previous studies accomplished in the last six years (2016–2022).
 (ii)
 Blind screening review process, which implies that the authors of this research developed this procedure to evaluate previous studies. The procedure was achieved by using the Rayyan method.
 (iii)
 Eligibility, as a full review of the documents was developed to identify relevant contributions to this study.
 (iv)
 Inclusion, as a quality analysis of selected documents from the eligibility stage was established. In Figure 2, an overview of the PRISMA methodology used for this systematic literature review is shown. Table 2 shows the distribution of previous studies, related to the Bayesian network methods in cybersecurity, found in journals, books, conferences and documents.
2.2. Risk Assessment Using Bayesian Networks
3. Risk Methodologies in Complex and Dynamic Environments
 Model complex systems;
 Manage unknown (latent) variables;
 Manage data lack;
 Use probability distributions;
 Use judgment experts;
 Direct conception of model structure.
 Identification and selection of nodes (factors). In scenarios where there is a lack of data for node modeling, the suggestion is to employ previous study cases or expert judgments.
 Define the model structure; this includes the relations (links) between nodes [60]. Define the causal relationship between nodes by a set of directed edges. The direction is from the origin nodes to the destination nodes.
 Determine the conditional probabilities of all nodes. Define prior elicitation from experts and/or from selected data.
 Validation of the model structure. Assess the feasibility and accuracy of the model by expert judgment.
4. Bayesian Network Structure
4.1. Key Factors of IoT Devices to Evaluate Risk Security
4.2. Bayesian Network Model
4.3. Probability Distribution of Bayesian Network Nodes
5. Results: Risk Security Using Scenario Cases and Bayesian Networks
6. Discussion and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
 World Economic Forum, Technology, Innovation and Systemic Risk. 2022. Available online: https://www.weforum.org/projects/technologyinnovationandsystemicrisk (accessed on 28 February 2022).
 Mckinsey. 2022. Available online: https://www.mckinsey.com/alumni/newsandinsights/globalnews/firmnews/theacceleratingvalueoftheinternetofthings (accessed on 28 February 2022).
 Zikria, Y.B.; Ali, R.; Afzal, M.K.; Kim, S.W. NextGeneration Internet of Things (IoT): Opportunities, Challenges, and Solutions. Sensors 2021, 21, 1174. [Google Scholar] [CrossRef] [PubMed]
 Radanliev, P.; De Roure, D.C.; Nurse, J.R.C.; Montalvo, R.M.; Cannady, S.; Santos, O.; Maddox, L.; Burnap, P.; Maple, C. Future developments in standardization of cyber risk in the Internet of Things (IoT). SN Appl. Sci. 2020, 2, 169. [Google Scholar] [CrossRef] [Green Version]
 Nurse, J.; Creese, S.; Roure, D. Security Risk Assessment in Internet of Things Systems. IT Prof. 2017, 19, 20–26. [Google Scholar] [CrossRef] [Green Version]
 Kandasamy, K.; Srinivas, S.; Achuthan, K.; Rangan, V.P. IoT cyber risk: A holistic analysis of cyber risk assessment frameworks, risk vectors, and risk ranking process. EURASIP J. Info. Secur. 2020, 2020, 8. [Google Scholar] [CrossRef]
 Deleuze, G.; Bertin, H.; Dutfoy, A.; Pierlot, S.; Pourret, O. Use of Bayesian Belief Networks for risk management in energy distribution. In Probabilistic Safety Assessment and Management; Spitzer, C., Schmocker, U., Dang, V.N., Eds.; Springer: London, UK, 2004. [Google Scholar] [CrossRef]
 Szpyrka, M.; Jasiul, B.; Wrona, K.; Dziedzic, F. Telecommunications Networks Risk Assessment with Bayesian Networks. In Computer Information Systems and Industrial Management. CISIM 2013. Lecture Notes in Computer Science; Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S., Eds.; Springer: Berlin/Heidelberg, German, 2013; Volume 8104. [Google Scholar] [CrossRef] [Green Version]
 Hunte, J.; Neil, M.; Fenton, N. Product risk assessment: A Bayesian network approach. arXiv 2020, arXiv:2010.06698. [Google Scholar]
 Li, M.; Hong, M.; Zhang, R. Improved Bayesian NetworkBased Risk Model and Its Application in Disaster Risk Assessment. Int. J. Disaster Risk Sci. 2018, 9, 237–248. [Google Scholar] [CrossRef] [Green Version]
 Pius, A.M.; Ogada, K.; Mwalili, T. Supervised Machine Learning Modelling of Demand for Outpatient HealthCare Services in Kenya using Artificial Neural Networks and Regression Decision Trees. In Proceedings of the 2021 22nd International Arab Conference on Information Technology (ACIT), Muscat, Oman, 21–23 December 2021; pp. 1–7. [Google Scholar] [CrossRef]
 Dahal, S.; Schaeffer, R.; Abdelfattah, E. Performance of Different Classification Models on National Coral Reef Monitoring Dataset. In Proceedings of the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 27–30 January 2021; pp. 0662–0666. [Google Scholar] [CrossRef]
 Chela, G.M.; Flores, M.; Gualli, T.G.; Andrade, R. Methodological Proposal for the Construction of a Decision Support System (DSS) Applied to IoT. In Information and Knowledge in Internet of Things. EAI/Springer Innovations in Communication and Computing; Guarda, T., Anwar, S., Leon, M., Mota Pinto, F.J., Eds.; Springer: Cham, Switzerland, 2022. [Google Scholar] [CrossRef]
 Jantsch, A.; Anzanpour, A.; Kholerdi, H.; Azimi, I.; Siafara, L.C.; Rahmani, A.M.; TaheriNejad, N.; Liljeberg, P.; Dutt, N. Hierarchical dynamic goal management for IoT systems. In Proceedings of the 2018 19th International Symposium on Quality Electronic Design (ISQED), Santa Clara, CA, USA, 13–14 March 2018; pp. 370–375. [Google Scholar] [CrossRef]
 Hongmei, L.; Wenning, H.; Wenyan, G.; Gang, C. Survey of Probabilistic Graphical Models. In Proceedings of the 2013 10th Web Information System and Application Conference, Washington, DC, USA, 10–15 November 2013; pp. 275–280. [Google Scholar] [CrossRef]
 Rabiner, L.; Juang, B. An introduction to hidden Markov models. IEEE ASSP Mag. 1986, 3, 4–16. [Google Scholar] [CrossRef]
 Cao, Y. Study of the Bayesian networks. In Proceedings of the 2010 International Conference on EHealth Networking Digital Ecosystems and Technologies (EDT), Shenzhen, China, 17–18 April 2010; pp. 172–174. [Google Scholar] [CrossRef]
 Kumar, P.; Singh, L.K.; Kumar, C.; Verma, S.; Kumar, S. A Bayesian Belief Network Model for Early Prediction of Reliability for ComputerBased SafetyCritical Systems. In Proceedings of the 2021 2nd International Conference on Range Technology (ICORT), Balasore, India, 5–6 August 2021; pp. 1–6. [Google Scholar] [CrossRef]
 Asvija, B.; Eswari, R.; Bijoy, M.B. Security Threat Modelling With Bayesian Networks and Sensitivity Analysis for IAAS Virtualization Stack. J. Organ. End User Comput. (JOEUC) 2021, 33, 44–69. [Google Scholar] [CrossRef]
 Guan, R.; Li, L.; Wang, T.; Qin, Y.; Xiong, W.; Liu, Q. A Bayesian Improved Defense Model for Deceptive Attack in HoneypotEnabled Networks. In Proceedings of the 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Zhangjiajie, China, 10–12 August 2019; pp. 208–214. [Google Scholar] [CrossRef]
 Kalnoor, G.; Gowrishankar, S. A model for intrusion detection system using hidden Markov and variational Bayesian model for IoT based wireless sensor network. Int. J. Inf. Tecnol. 2021, 14, 2021–2033. [Google Scholar] [CrossRef]
 Toğaçar, M. Detecting attacks on IoT devices with probabilistic Bayesian neural networks and hunger games search optimization approaches. Trans. Emerg. Telecommun. Technol. 2022, 33. [Google Scholar] [CrossRef]
 Kalnoor, G.; Gowrishankar, S. A Framework Using MarkovBayes’ Model for Intrusion Detection in Wireless Sensor Network. In ICDSMLA 2020; Lecture Notes in Electrical, Engineering; Kumar, A., Senatore, S., Gunjan, V.K., Eds.; Springer: Singapore, 2022; Volume 783. [Google Scholar] [CrossRef]
 Wisanwanichthan, T.; Thammawichai, M. A DoubleLayered Hybrid Approach for Network Intrusion Detection System Using Combined Naive Bayes and SVM. IEEE Access 2021, 9, 138432–138450. [Google Scholar] [CrossRef]
 Liu, Q.; Keller, H.B.; Hagenmeyer, V. A Bayesian Rule Learning Based Intrusion Detection System for the MQTT Communication Protocol. In Proceedings of the 16th International Conference on Availability, Reliability and Security (ARES 2021), Vienna, Austria, 17–20 August 2021; Association for Computing Machinery: New York, NY, USA, 2021; Volume 81, pp. 1–10. [Google Scholar] [CrossRef]
 Sahu, A.; Davis, K. Structural Learning Techniques for Bayesian Attack Graphs in Cyber Physical Power Systems. In Proceedings of the 2021 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, USA, 2–5 February 2021; pp. 1–6. [Google Scholar] [CrossRef]
 Klassen, M.; Yang, N. Anomaly based intrusion detection in wireless networks using Bayesian classifier. In Proceedings of the 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), Nanjing, China, 18–20 October 2012; pp. 257–264. [Google Scholar] [CrossRef]
 Berguig, Y.; Laassiri, I.; Hanaoui, S. DoS Detection Based on Mobile Agent and Naïve Bayes Filter. In Proceedings of the 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Kenitra, Morocco, 21–23 November 2018; pp. 1–6. [Google Scholar] [CrossRef]
 Fu, Y.; He, Z. BayesianInferenceBased Sliding Window Trust Model Against Probabilistic SSDF Attack in Cognitive Radio Networks. IEEE Syst. J. 2020, 14, 1764–1775. [Google Scholar] [CrossRef]
 MuñozGonzález, L.; Sgandurra, D.; Barrère, M.; Lupu, E.C. Exact Inference Techniques for the Analysis of Bayesian Attack Graphs. IEEE Trans. Dependable Secur. Comput. 2019, 16, 231–244. [Google Scholar] [CrossRef] [Green Version]
 Vaddi, P.K.; Pietrykowski, M.C.; Kar, D.; Diao, X.; Zhao, Y.; Mabry, T.; Ray, I.; Smidts, C. Dynamic bayesian networks based abnormal event classifier for nuclear power plants in case of cyber security threats. Prog. Nucl. Energy 2020, 128, 103479. [Google Scholar] [CrossRef]
 Lin, P.; Chen, Y. Dynamic Network Security Situation Prediction based on Bayesian Attack Graph and Big Data. In Proceedings of the 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 14–16 December 2018; pp. 992–998. [Google Scholar] [CrossRef]
 Zhang, Y.; Malacaria, P. Bayesian Stackelberg games for cybersecurity decision support. Decis. Support Syst. 2021, 148, 113599. [Google Scholar] [CrossRef]
 Durgadevi, V.; Ganeshkumar, P. Fuzzy integrated Bayesian DempsterShafer Theory to defend crosslayer heterogeneity attacks in Communication Network of Smart Grid. Inf. Sci. 2018, 479, 542–566. [Google Scholar] [CrossRef]
 Alhakami, W.; Lharbi, A.A.; Bourouis, S.; Alroobaea, R.; Bouguila, N. Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection. IEEE Access 2019, 7, 52181–52190. [Google Scholar] [CrossRef]
 Pirbhulal, S.; Gkioulos, V.; Katsikas, S. Towards Integration of Security and Safety Measures for Critical Infrastructures Based on Bayesian Networks and Graph Theory: A Systematic Literature Review. Signals 2021, 2, 771–802. [Google Scholar] [CrossRef]
 Forti, N.; Battistelli, G.; Chisci, L.; Sinopoli, B. A Bayesian approach to joint attack detection and resilient state estimation. In Proceedings of the 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, USA, 12–14 December 2016; pp. 1192–1198. [Google Scholar] [CrossRef]
 Li, Y.; Liu, T.; Zhu, J.; Wang, X. IoT Security Situational Awareness Based on QLearning and Bayesian Game; Springer: Singapore, 2021; pp. 190–203. ISBN 9789811659430. [Google Scholar]
 Yesi, K.; Siti, N.; Deris, S.; Bhakti, Y. Improving Classification Attacks in IOT Intrusion Detection System using Bayesian Hyperparameter Optimization. In Proceedings of the 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia, 10 December 2020; pp. 146–151. [Google Scholar] [CrossRef]
 Wang, J.; Guo, M. Vulnerability categorization using Bayesian networks. In Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research (CSIIRW ′10), Oak Ridge, TN, USA, 21–23 April 2010; Association for Computing Machinery: New York, NY, USA, 2010; Volume 29, pp. 1–4. [Google Scholar] [CrossRef]
 Priscilla, G.; Vadakkapaikkadu, R. Evolution of Safety and Security Risk Assessment methodologies to use of Bayesian Networks in Process Industries. Process Saf. Environ. Prot. 2021, 149, 758–775. [Google Scholar] [CrossRef]
 Hui, B.F.; Ma, Y.L. Information Security Defense Evaluation Based on Bayesian Network. In Proceedings of the International Conference on Artificial Intelligence for Communications and Networks, Xining, China, 23–24 October 2021; Springer: Cham, Switzerland, 2021; pp. 3–7, ISBN 9783030901998. [Google Scholar]
 Wang, J.; Fan, K.; Mo, W.; Xu, D. A Method for Information Security Risk Assessment Based on the Dynamic Bayesian Network. In Proceedings of the 2016 International Conference on Networking and Network Applications (NaNA), Hakodate City, Japan, 23–25 July 2016; pp. 279–283. [Google Scholar] [CrossRef]
 Behfarnia, A.; Eslami, A. Risk Assessment of Autonomous Vehicles Using Bayesian Defense Graphs. In Proceedings of the 2018 IEEE 88th Vehicular Technology Conference (VTCFall), Chicago, IL, USA, 27–30 August 2018; pp. 1–5. [Google Scholar] [CrossRef] [Green Version]
 Isaac, M.; Sadegh, S.; Aad, M. Stochastic Simulation Techniques for Inference and Sensitivity Analysis of Bayesian Attack Graphs. In Proceedings of the International Conference on Science of Cyber Security, Shanghai, China, 13–15 August 2021; Springer: Cham, Switzerland, 2021. [Google Scholar]
 Zhang, Q.; Zhou, C.; Tian, Y.C.; Xiong, N.; Qin, Y.; Hu, B. A Fuzzy Probability Bayesian Network Approach for Dynamic Cybersecurity Risk Assessment in Industrial Control Systems. IEEE Trans. Ind. Inform. 2018, 14, 2497–2506. [Google Scholar] [CrossRef]
 Halabi, T.; Wahab, O.A.; Al Mallah, R.; Zulkernine, M. Protecting the Internet of Vehicles Against Advanced Persistent Threats: A Bayesian Stackelberg Game. IEEE Trans. Reliab. 2021, 70, 970–985. [Google Scholar] [CrossRef]
 Thakkar, A.; Badsha, S.; Sengupta, S. Game theoretic approach applied in cybersecurity information exchange framework. In Proceedings of the 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 10–13 January 2020; pp. 1–7. [Google Scholar] [CrossRef]
 Wall, A.; Agrafiotis, I. A Bayesian approach to insider threat detection. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2021, 12, 48–84. [Google Scholar]
 Wahab, O.A.; Bentahar, J.; Otrok, H.; Mourad, A. ResourceAware Detection and Defense System against MultiType Attacks in the Cloud: Repeated Bayesian Stackelberg Game. IEEE Trans. Dependable Secur. Comput. 2021, 18, 605–622. [Google Scholar] [CrossRef]
 Hu, Z.; Yu, X.; Shi, J.; Ye, L. Abnormal Event Correlation and Detection Based on Network Big Data Analysis. Comput. Mater. Contin. 2021, 69, 695–711. [Google Scholar] [CrossRef]
 Yang, C.; Shi, Z.; Zhang, H.; Wu, J.; Shi, X. Multiple Attacks Detection in CyberPhysical Systems Using Random Finite Set Theory. IEEE Trans. Cybern. 2020, 50, 4066–4075. [Google Scholar] [CrossRef] [PubMed]
 Peng, Q. Bayesian Networks for Data Prediction. In Proceedings of the 2009 International Forum on Computer ScienceTechnology and Applications, ChongQing, China, 25–27 December 2009; pp. 101–102. [Google Scholar] [CrossRef]
 Radanliev, P.; de Roure, D.; Cannady, S.; Montalvo, R.M.; Nicolescu, R.; Huth, M. Economic impact of IoT cyber risk—Analysing past and present to predict the future developments in IoT risk analysis and IoT cyber insurance. In Living in the Internet of Things: Cybersecurity of the IoT2018; Institution of Engineering and Technology: London, UK, 2018; pp. 1–9. [Google Scholar] [CrossRef] [Green Version]
 Bahizad, S. Risks of Increase in the IoT Devices. In Proceedings of the 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), New York, NY, USA, 1–3 August 2020; pp. 178–181. [Google Scholar] [CrossRef]
 Wangyal, S.; Dechen, T.; Tanimoto, S.; Sato, H.; Kanai, A. A Study of Multiviewpoint Risk Assessment of Internet of Things (IoT). In Proceedings of the 2020 9th International Congress on Advanced Applied Informatics (IIAIAAI), Kitakyushu, Japan, 1–15 September 2020; pp. 639–644. [Google Scholar] [CrossRef]
 Al Mousa, A.; al Qomri, M.; al Hajri, S.; Zagrouba, R.; Chaabani, S. Environment Based IoT Security Risks and Vulnerabilities Management. In Proceedings of the 2020 International Conference on Computing and Information Technology (ICCIT1441), Tabuk, Saudi Arabia, 9–10 September 2020; pp. 1–6. [Google Scholar] [CrossRef]
 Kononenko, I.; Kukar, M. Chapter 3—Machine Learning Basics. In Machine Learning and Data Mining; Igor, K., Matjaž, K., Eds.; Woodhead Publishing: Sawston, Cambridge, 2007; pp. 59–105. ISBN 9781904275213. [Google Scholar] [CrossRef]
 Scanagatta, M.; Salmerón, A.; Stella, F. A survey on Bayesian network structure learning from data. Prog. Artif. Intell. 2019, 8, 425–439. [Google Scholar] [CrossRef]
 Piccininni, M.; Konigorski, S.; Rohmann, J.L.; Kurth, T. Directed acyclic graphs and causal thinking in clinical risk prediction modeling. BMC Med. Res. Methodol. 2020, 20, 179. [Google Scholar] [CrossRef]
 Devore, J.L.; Berk, K.N.; Carlton, M.A. Joint Probability Distributions and Their Applications. In Modern Mathematical Statistics with Applications. Springer Texts in Statistics; Springer: Cham, Switzerland, 2021. [Google Scholar] [CrossRef]
 Mikkola, P.; Martin, O.; Chandramouli, S.; Hartmann, M.; Pla, O.; Thomas, O.; Pesonen, H.; Corander, J.; Vehtari, A.; Kaski, S.; et al. Prior knowledge elicitation: The past, present, and future. arXiv 2021, arXiv:2112.01380. [Google Scholar]
 Xu, S.; Jia, B.; Liang, F. Learning Moral Graphs in Construction of HighDimensional Bayesian Networks for Mixed Data. Neural Comput. 2019, 31, 1183–1214. [Google Scholar] [CrossRef]
Regression  Classification  Neuronal Networks  Probabilistic Graphical Models 

Decision tree regression (CART)  Logistic regression  Autoencoders  Bayesian belief net work 
Random forest regression  Adaptive boosting (AdaBoost)  Conventional neural networks  Hidden Markov model 
Knearest neighbors regression (KNN)  Naïve Bayes  Recurrent neural net works  
Multivariate adaptive regression splines (MARS)  Support vector machine (SVM)  
Support vector regression (SVR) 
Type of Manuscripts  Number of Works  Topics Related to Bayesian Networks 

Journal  348  
Conference  210  
Book  3  
Chapters  92 
Application Areas  Number of Papers  Focus On 

IoT  47  Detecting attacks [37]. Situational awareness [38]. Classification of attacks [39]. Classification of vulnerabilities [40]. 
Risk management/assessment  34  Industrial process [41]. Information security [42]. Network systems [43]. Cyber–physical systems [22]. Autonomous vehicles [24]. Attack graphs [44]. Cybersecurity protection [45]. 
Awareness  5  IoT security situational awareness [38]. Information attack in vehicular ad hoc network [24]. 
Defense mechanism  4  Advanced persistent threats [46]. Game theoretical approach [47]. 
Detection of attacks  158  Insider threat detection [48]. Resourceaware detection [49]. Detection in a cloud environment [50]. Abnormal event correlation [51]. Multiple attacks detection [52]. 
Factors  Description 

Vulnerabilities  The IoT device may have vulnerabilities in its layers (three on the ITU model). Therefore, the vulnerability value of an IoT model represents the overall value of all contributions in each layer. 
Type of attack  Different types of attacks can compromise the confidentiality, availability and integrity of IoT devices. 
Attack surface  The attack surface will be conditioned by the inherent organization characteristics in which the IoT solution has been implemented. The attack surface includes entry/exit points, transmission channels, protocols and data used in the IoT model layers (three layers in the ITU case). The number of used IoT devices can also increase the attack surface due to the growing number of entry/exit points, channels, protocols and data. 
Interdependency  The IoT device interacts with different layers’ protocols and technologies employed on the IoT system. The IoT device serves to build solutions that have a social, economic and environmental impact on the organization’s domain or pillar. Interdependency is driven with other IT/OT systems or IoT systems to implement the required functionalities. This interdependency between domains and systems increases the attack’s surface. 
Severity  The severity will depend on the confidentiality, availability and integrity impact of the operations and information handled by the IoT device. The severity and security components impact (CIA) will depend on the target and type of attack. For example, an MITM attack will be focused on confidentiality, while a DoS attack will be focused on availability. The IoT device security protection–CIA will depend on the security requirements arising from the inherent characteristics of the domain or pillar. The vulnerability’s presence can increase the likelihood of a significant impact on security components during an attack. 
Application domain  The attack on IoT devices could affect economic, social and environmental operations. The domain or pillar requires certain security configurations, and it may have inherent vulnerabilities. The characteristics of the domain or location may increase the attacked IoT device’s susceptibility. 
Scalability  The behavior of the security attack may be conditioned by the IoT device’s dependency on other IT/OT systems. The attack could come from IT/OT to the IoT, or vice versa. This could increase the attack’s scalability. A higher number of devices could also increase attack scalability. Previous episodes could generate higherimpact attacks. 
Susceptibility  The attack susceptibility is linked to the IoT device’s susceptibility. The IoT device may have components in different layers (according to the ITU model: three layers), which could increase the attack susceptibility due to extra entry and exit points. The systems’ interdependence could also affect the susceptibility. Exposure to a higher number of attacks and a shorter time between them can negatively affect equipment susceptibility. 
Uncertainty  The security attack’s effect on IoT systems can have a random behavior depending on different variables, such as attack transmission through IoT devices. There is a nondeterministic behavior to the attack because it is not possible to precisely establish the security condition of the IoT device or IT/OT system at the time of the attack. 
Nodes  Nodes Status 

Vulnerabilities 

Attack surface 

Interdependency 

Application domain 

Susceptibility 

Nodes of Application Domain  Nodes Status 

Domain  (1) impact; (2) no impact 
Pillar  (1) impact; (2) no impact 
Economic  (1) impact; (2) no impact 
Environmental  (1) impact; (2) no impact 
Social  (1) impact; (2) no impact 
Number Nodes  Computational Time (Seconds) 

1  12 s 
2  12 s 
3  12.5 s 
4  15 s 
Case  Nodes  Description 

VS  vulnerability; susceptibility  vulnerability = exist; susceptibility = exist 
VI  vulnerability; interdependency  vulnerability = exist; interdependency = exist 
VIAs  vulnerability; interdependency; attack surface  vulnerability = exist; interdependency = exist; attack surface = hackable 
VSAsI  vulnerability; susceptibility; attack surface; interdependency  vulnerability = exist; susceptibility = exist; attack surface = hackable; interdependency = exist 
VSI  vulnerability; susceptibility; interdependency  vulnerability = exist; susceptibility = exist; interdependency = exist 
IoT Factors (Input Variables)  Impact (Output Variables)  

Vulnerability  Susceptibility  Attack Surface  Interdependency  Economic  Social  Environmental 
70%  50%  60%  60%  70.77%  63.98%  55.90% 
100%  50%  50%  60%  73.12%  66.04%  57.66% 
100%  100%  50%  60%  76.56%  69.08%  60.26% 
100%  100%  100%  60%  77.91%  70.25%  61.26% 
100%  100%  100%  100%  86.05%  77.15%  67.28% 
70%  100%  50%  60%  73.40%  66.30%  57.88% 
70%  50%  50%  100%  84.86%  76.22%  66.43% 
Factors  Vulnerability  Attack Surface  Susceptibility  Interdependency  IoT Risk Security 

Weights  0.32  0.06  0.13  0.49  6.19 
Values  8  5  3  6 
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Andrade, R.; Ortiz, I.; Cazares, M.; Navas, G.; SánchezPazmiño, M.I. Defining Cyber Risk Scenarios to Evaluate IoT Systems. Games 2023, 14, 1. https://doi.org/10.3390/g14010001
Andrade R, Ortiz I, Cazares M, Navas G, SánchezPazmiño MI. Defining Cyber Risk Scenarios to Evaluate IoT Systems. Games. 2023; 14(1):1. https://doi.org/10.3390/g14010001
Chicago/Turabian StyleAndrade, Roberto, Iván Ortiz, María Cazares, Gustavo Navas, and María Isabel SánchezPazmiño. 2023. "Defining Cyber Risk Scenarios to Evaluate IoT Systems" Games 14, no. 1: 1. https://doi.org/10.3390/g14010001