Integration of Cloud Computing with Internet of Things: Challenges and Open Issues

Atlam, Hany F.; Alenezi, Ahmed; Alharthi, Abdulrahman; Walters, Robert John; Wills, Gary

doi:10.1109/ithings-greencom-cpscom-smartdata.2017.105

Cited by 144 publications

(80 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Through cheap sensors, a lot of information can be collected from the surrounding environment that results in improving our life. Protecting IoT devices and their communication channels become a mandatory task to prevent sensitive information disclosure which can lead to literally lose lives [27,31]. The access control is used to protect system resources by limiting the access only to authorized users [32,33].…”

Section: Risk-based Access Control Model For Iotmentioning

confidence: 99%

Fuzzy Logic with Expert Judgment to Implement an Adaptive Risk-Based Access Control Model for IoT

et al. 2019

Self Cite

View full text Add to dashboard Cite

The Internet of Things (IoT) is becoming the future of the Internet with a large number of connected devices that are predicted to reach about 50 billion by 2020. With proliferation of IoT devices and need to increase information sharing in IoT applications, risk-based access control model has become the best candidate for both academic and commercial organizations to address access control issues. This model carries out a security risk analysis on the access request by using IoT contextual information to provide access decisions dynamically. This model solves challenges related to flexibility and scalability of the IoT system. Therefore, we propose an adaptive risk-based access control model for the IoT. This model uses real-time contextual information associated with the requesting user to calculate the security risk regarding each access request. It uses user attributes while making the access request, action severity, resource sensitivity and user risk history as inputs to analyze and calculate the risk value to determine the access decision. To detect abnormal and malicious actions, smart contracts are used to track and monitor user activities during the access session to detect and prevent potential security violations. In addition, as the risk estimation process is the essential stage to build a risk-based model, this paper provides a discussion of common risk estimation methods and then proposes the fuzzy inference system with expert judgment as to be the optimal approach to handle risk estimation process of the proposed risk-based model in the IoT system.

show abstract

Section: Risk-based Access Control Model For Iotmentioning

confidence: 99%

Fuzzy Logic with Expert Judgment to Implement an Adaptive Risk-Based Access Control Model for IoT

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The integration of Cloud Computing and IoT was referred to as CloudIoT [1] and it is expected to be a disruptive technology as IoT able to use the vast resources provided by the Cloud. On the other hand, the Cloud can extend its scope through IoT and deliver new services A general view about the integration concept of the cloud with IoT (as means to overcome the challenges of IoT related to data storage, scalability, and energy efficiency) is presented in [5], where it is mentioned that the huge amount of resources available on the cloud can help the limited capabilities of IoT. An added advantage is the quick lowcost installation and integration for complex data processing.…”

Section: Figure 1 Interaction Of Iot With Cloud Via Internet [3]mentioning

confidence: 99%

“…Therefore, Cloud Computing has strong sources of data processing from Internet objects and large storage space. Users do not need to worry about buying storage because the storage is scaled as the data generated increases and the user pays only for the consumed storage [4,5]. It is estimated that, in the next two decades, the demand for Cloud Computing services will be further increased.…”

Section: Introductionmentioning

confidence: 99%

Efficient Resource Allocation for Time-Sensitive IoT Applications in Cloud and Fog Environments

Kazem*¹

2019

IJRTE

View full text Add to dashboard Cite

Nowadays, with the quick development of internet and cloud technologies, a big number of physical objects are linked to the Internet and every day, more objects are connected to the Internet. It provides great benefits that lead to a significant improvement in the quality of our daily life. Examples include: Smart City, Smart Homes, Autonomous Driving Cars or Airplanes and Health Monitoring Systems. On the other hand, Cloud Computing provides to the IoT systems a series of services such as data computing, processing or storage, analysis and securing. It is estimated that by the year 2025, approximately trillion IoT devices will be used. As a result, a huge amount of data is going to be generated. In addition, in order to efficiently and accurately work, there are situations where IoT applications (such as Self Driving, Health Monitoring, etc.) require quick responses. In this context, the traditional Cloud Computing systems will have difficulties in handling and providing services. To balance this scenario and to overcome the drawbacks of cloud computing, a new computing model called fog computing has proposed. In this paper, a comparison between fog computing and cloud computing paradigms were performed. The scheduling task for an IoT application in a cloud-fog computing system was considered. For the simulation and evaluation purposes, theCloudAnalyst simulation toolkit was used. The obtained numerical results showed the fog computing achieves better performance and works more efficient than Cloud computing. It also reduced the response time, processing time ,and cost of transfer data to the cloud.

show abstract

“…According to [34], [35] Fig.1, user/agent context, resource sensitivity, action severity and risk history. These inputs/risk factors are used to estimate the security risk associated with each access request.…”

Section: Access Control Requirements In the Iotmentioning

confidence: 99%

Validation of an Adaptive Risk-based Access Control Model for the Internet of Things

Atlam¹,

Alenezi²,

Hussein³

et al. 2018

IJCNIS

Self Cite

View full text Add to dashboard Cite

Abstract-The Internet of Things (IoT) has spread into multiple dimensions that incorporate different physical and virtual things. These things are connected together using different communication technologies to provide unlimited services. These services help not only to improve the quality of our daily lives, but also to provide a communication platform for increasing object collaboration and information sharing. Like all new technologies, the IoT has many security challenges that stand as a barrier to the successful implementation of IoT applications. These challenges are more complicated due to the dynamic and heterogeneous nature of IoT systems. However, authentication and access control models can be used to address the security issue in the IoT. To increase information sharing and availability, the IoT requires a dynamic access control model that takes not only access policies but also real-time contextual information into account when making access decisions. One of the dynamic features is the security risk. This paper proposes an Adaptive Risk-Based Access Control (AdRBAC) model for the IoT and discusses its validation using expert reviews. The proposed AdRBAC model conducts a risk analysis to estimate the security risk value associated with each access request when making an access decision. This model has four inputs/risk factors: user context, resource sensitivity, action severity and risk history. These risk factors are used to estimate a risk value associated with the access request to make the access decision. To provide the adaptive features, smart contracts will be used to monitor the user behaviour during access sessions to detect any malicious actions from the granted users. To validate and refine the proposed model, twenty IoT security experts from inside and outside the UK were interviewed. The experts have suggested valuable information that will help to specify the appropriate risk factors and risk estimation technique for implantation of the AdRBAC model.

show abstract

Integration of Cloud Computing with Internet of Things: Challenges and Open Issues

Cited by 144 publications

References 21 publications

Fuzzy Logic with Expert Judgment to Implement an Adaptive Risk-Based Access Control Model for IoT

Fuzzy Logic with Expert Judgment to Implement an Adaptive Risk-Based Access Control Model for IoT

Efficient Resource Allocation for Time-Sensitive IoT Applications in Cloud and Fog Environments

Validation of an Adaptive Risk-based Access Control Model for the Internet of Things

Contact Info

Product

Resources

About