Integration of AI and IoT Approaches for Evaluating Cybersecurity Risk on Smart City

Andrade, Roberto; Yoo, Sang Guun; Tello-Oquendo, Luis; Flores, Miguel; Ortiz, Iván

doi:10.1007/978-3-030-87059-1_12

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Bayesian Networks (BN) provide a useful mechanism in the risk analysis field due to their ability to model probabilistic data. Andrade et al [18] states that dynamic BNs take into consideration temporal dependencies based on time. Basic BNs do not consider alterations in time or manage time-evolving environments; thus, dynamic BNs (DBNs) are ideal for handling time-dependent risk assessments.…”

Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment

Behbehani

Komninos

Al-Begain

et al. 2022

Preprint

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Cloud computing adoption has been increasing rapidly amid COVID-19 as organisations accelerate the implementation of their digital strategies. Most models adopt traditional dynamic risk assessment, which does not adequately quantify or monetise risks to enable business-appropriate decision-making. To address this issue, we propose a Cloud Enterprise Dynamic Risk Assessment (CEDRA) model that uses CVSS, threat intelligence feeds and information about exploitation availability in the wild using dynamic Bayesian networks to predict vulnerability exploitations and financial losses. We evaluate the proposed model in a real case scenario that demonstrates the applicability of this model.

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Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment

Behbehani

Komninos

Al-Begain

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Bayesian Networks (BN) provide a useful mechanism in the risk analysis field due to their ability to model probabilistic data. [5] states that dynamic BNs take into consideration temporal dependencies based on time. Basic BNs do not consider alterations in time or manage time-evolving environments; thus, dynamic BNs (DBNs) are ideal for handling time-dependent risk assessments.…”

Section: Bayesian Networkmentioning

confidence: 99%

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment

Behbehani

Komninos

Al‐Begain

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Risk factors can be reduced through rigorous safety and assessment. The risk analysis model should be intelligent and adaptable to any type of environment [22]. There is the potential for risk in adaptive security operations, and this is how we introduce risk analysis to them.…”

Section: Introductionmentioning

confidence: 99%

Applying Adaptive Security Techniques for Risk Analysis of Internet of Things (IoT)-Based Smart Agriculture

et al. 2022

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In modern times, the Internet of Things (IoT) is having a major impact on agriculture. Risk and security parameters are always linked when researching, developing, implementing and deploying IoT-based devices. It is a myth that security does not play a major role in IoT applications in agriculture. Data accuracy and availability is a high-priority requirement for farmers who help achieve high yields. A secure IoT network requires a situational approach, often referred to as dynamic security. An advanced security approach to improving IoT security is adaptive security, a cybersecurity-based approach. The lack of security in a smart farming environment is a very important factor for agricultural growth. In this study, we introduce IoT together with adaptive security operations and integrate it into a smart farming environment. We propose an evaluation framework that can be applied to diverse smart farming environments. Several scenarios of an agricultural environment with smart devices and sensors are described for execution. Storylines with real-time environments are then derived from these scenarios to extend and incorporate adaptive security frameworks and scenarios in IoT-based agriculture.

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Integration of AI and IoT Approaches for Evaluating Cybersecurity Risk on Smart City

Cited by 6 publications

References 28 publications

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment

Applying Adaptive Security Techniques for Risk Analysis of Internet of Things (IoT)-Based Smart Agriculture

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