Defining Cyber Risk Scenarios to Evaluate IoT Systems
- 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 real-time 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
- 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).
- 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.
- Eligibility, as a full review of the documents was developed to identify relevant contributions to this study.
- 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 . 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
Conflicts of Interest
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|Regression||Classification||Neuronal Networks||Probabilistic Graphical Models|
|Decision tree regression (CART)||Logistic regression||Autoencoders||Bayesian belief net |
|Random forest regression||Adaptive boosting |
|Conventional neural |
|Hidden Markov model|
|K-nearest neighbors regression (KNN)||Naïve Bayes||Recurrent neural net |
|Multivariate adaptive regression splines (MARS)||Support vector machine |
|Support vector regression (SVR)|
|Type of Manuscripts||Number of Works||Topics Related to Bayesian Networks|
|Application Areas||Number of Papers||Focus On|
|IoT||47||Detecting attacks .|
Situational awareness .
Classification of attacks .
Classification of vulnerabilities .
|Risk management/assessment||34||Industrial process .|
Information security .
Network systems .
Cyber–physical systems .
Autonomous vehicles .
Attack graphs .
Cybersecurity protection .
|Awareness||5||IoT security situational awareness .|
Information attack in vehicular ad hoc network .
|Defense mechanism||4||Advanced persistent threats .|
Game theoretical approach .
|Detection of attacks||158||Insider threat detection .|
Resource-aware detection .
Detection in a cloud environment .
Abnormal event correlation .
Multiple attacks detection .
|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 higher-impact 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 non-deterministic 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 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)|
|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)|
|Factors||Vulnerability||Attack Surface||Susceptibility||Interdependency||IoT Risk Security|
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Andrade, R.; Ortiz, I.; Cazares, M.; Navas, G.; Sánchez-Pazmiño, 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, Sánchez-Pazmiño MI. Defining Cyber Risk Scenarios to Evaluate IoT Systems. Games. 2023; 14(1):1. https://doi.org/10.3390/g14010001Chicago/Turabian Style
Andrade, Roberto, Iván Ortiz, María Cazares, Gustavo Navas, and María Isabel Sánchez-Pazmiño. 2023. "Defining Cyber Risk Scenarios to Evaluate IoT Systems" Games 14, no. 1: 1. https://doi.org/10.3390/g14010001