3aRAM: A 3-Layer AHP-Based Risk Assessment Model and its Implementation for an Industrial IoT Cloud

Huang, Yu‐Lun; Sun, Wen-Lin; Tang, Ying‐Han

doi:10.1109/qrs-c.2019.00087

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…This paper extends the previous conference paper [20], presenting a three‐layer risk assessment model named 3aRAM. Depending on the practical experience absorbed from Industrial Technology Research Institute's NIP and the survey of existing IoT and cloud risk assessment approaches, we revise the structure of 3aRAM and improve its flexibility and scalability to meet the requirements in real‐world scenarios.…”

Section: Introductionsupporting

confidence: 71%

HiRAM: A hierarchical risk assessment model and its implementation for an industrial Internet of Things in the cloud

Sun

Tang

Huang

2023

Software Testing Verif & Rel

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The trend towards intelligent control processes has introduced the Internet of Things (IoT) and cloud computing technologies to factories. IoT devices can sense data and send it to a cloud for further analysis in a factory. Consequently, the quantity of such valuable data flowing in an industrial cyber‐physical system has gradually increased. Tailoring a risk assessment system for Industrial IoT (IIoT) is essential, particularly for a cloud platform that handles the IIoT data flow. In this study, we leverage analytic hierarchy processes (AHPs) and propose Hierarchical Risk Assessment Model (HiRAM) for an IIoT cloud platform. The proposed model allows the platform to self‐evaluate its security status. Furthermore, a modular and responsive Risk Assessment System based on HiRAM, called HiRAM‐RAS, is realized and evaluated in a real‐world IIoT cloud platform. We deploy HiRAM‐RAS to a sample application and introduce the practical deployment procedures. We then estimate the practicality of the HiRAM‐RAS by injecting different degrees of errors and launching Distributed denial‐of‐service (DDoS) attacks. The results demonstrate the changes in integrity and availability scores evaluated by HiRAM.

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Section: Introductionsupporting

confidence: 71%

HiRAM: A hierarchical risk assessment model and its implementation for an industrial Internet of Things in the cloud

Sun

Tang

Huang

2023

Software Testing Verif & Rel

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“…Huang et al [27] proposed an Analytical Hierarchy Process (AHP) analytical risk assessment model using a one-class Support Vector Machine (SVM). It is a machine learning algorithm used for classifications.…”

Section: ) Ahp -Svmmentioning

confidence: 99%

“…5. Three Layer AHP-SVM Risk Assessment Model [27] From the review of these IoT Risk Assessment Models, firstly most of the risk assessment model involves many steps; hence it will take much time to complete. Hence, these extensive processes will be ineffective with a high paced and dynamic system of IoT itself.…”

Section: ) Ahp -Svmmentioning

confidence: 99%

A Conceptual Model for Internet of Things Risk Assessment in Healthcare Domain with Deep Learning Approach

et al. 2020

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The Internet of Things (IoT) has become a prevalent technology in the IT industry. One of the industries that can benefit extensively in this technology is healthcare. However, the healthcare IoT is still under debate with several studies suggesting it is lack of interoperability, security, and too much complexity. Even more, the risk involved in deploying it is still enormous. Many traditional risk assessment models are unable to provide a specific IoT risk guideline and specification, especially in the healthcare area. Thus, it is essential to understand the full extent of the IoT risk and how to manage its risk in the healthcare area. The risk management models, such as NIST SP 800-30, ISO/IEC 27005, OCTAVE, CRAMM, and EBIOS, which are among the leading and widely used in many areas and healthcare fields, have also been described. Besides, this paper includes a review of three IoT risk assessment models that are based on ABA-IDS, Deep Learning, and AHP-SVM. Based on the review analysis, we proposed a new enhanced healthcare IoT risk assessment model, which aims to provide a real-time monitoring and mitigating risks that incorporate the NIST SP 800-30 framework, ABA-IDS, and CNN deep learning. This shall constitute a better classification of each risk identified to find the best risk mitigation plan.

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Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations

Yaacoub

Noura

Salman

et al. 2021

Int. J. Inf. Secur.

145

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The recent digital revolution led robots to become integrated more than ever into different domains such as agricultural, medical, industrial, military, police (law enforcement), and logistics. Robots are devoted to serve, facilitate, and enhance the human life. However, many incidents have been occurring, leading to serious injuries and devastating impacts such as the unnecessary loss of human lives. Unintended accidents will always take place, but the ones caused by malicious attacks represent a very challenging issue. This includes maliciously hijacking and controlling robots and causing serious economic and financial losses. This paper reviews the main security vulnerabilities, threats, risks, and their impacts, and the main security attacks within the robotics domain. In this context, different approaches and recommendations are presented in order to enhance and improve the security level of robotic systems such as multi-factor device/user authentication schemes, in addition to multi-factor cryptographic algorithms. We also review the recently presented security solutions for robotic systems.

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3aRAM: A 3-Layer AHP-Based Risk Assessment Model and its Implementation for an Industrial IoT Cloud

Cited by 4 publications

References 17 publications

HiRAM: A hierarchical risk assessment model and its implementation for an industrial Internet of Things in the cloud

HiRAM: A hierarchical risk assessment model and its implementation for an industrial Internet of Things in the cloud

A Conceptual Model for Internet of Things Risk Assessment in Healthcare Domain with Deep Learning Approach

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations

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