A Systematic Mapping Study of Access Control in the Internet of Things

Ragothaman, Kaushik; Wang, Yong

doi:10.24251/hicss.2021.853

Cited by 3 publications

(4 citation statements)

References 37 publications

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“…The requirements in [ 9 ] are not specific to the IoT and may apply to other devices. Based on the work in [ 9 , 16 ], we further refined these requirements based on the characteristics of the IoT, including but not limited to the variety of the IoT devices, the resource constraints on the IoT devices, and the heterogeneous nature of the IoT. The twelve refined requirements, which are more specific to the IoT, are summarized below.…”

Section: Access Control In the Iotmentioning

confidence: 99%

“…The security flaws in the access control may occur in many places, including design, protocols, implementations and configurations. Although many access control models have been proposed for IoT, limited research has been conducted on access control process security analysis [ 16 ]. Due to the importance of access control for any network, access control security analysis is desired.…”

Section: Access Control Research Challenges and Future Directionsmentioning

confidence: 99%

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Access Control for IoT: A Survey of Existing Research, Dynamic Policies and Future Directions

Ragothaman

Wang

Rimal

et al. 2023

Sensors

Self Cite

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Internet of Things (IoT) provides a wide range of services in domestic and industrial environments. Access control plays a crucial role in granting access rights to users and devices when an IoT device is connected to a network. However, many challenges exist in designing and implementing an ideal access control solution for the IoT due to the characteristics of the IoT including but not limited to the variety of the IoT devices, the resource constraints on the IoT devices, and the heterogeneous nature of the IoT. This paper conducts a comprehensive survey on access control in the IoT, including access control requirements, authorization architecture, access control models, access control policies, access control research challenges, and future directions. It identifies and summarizes key access control requirements in the IoT. The paper further evaluates the existing access control models to fulfill the access control requirements. Access control decisions are governed by access control policies. The existing approaches on dynamic policies’ specification are reviewed. The challenges faced by the existing solutions for policies’ specification are highlighted. Finally, the paper presents the research challenges and future directions of access control in the IoT. Due to the variety of IoT applications, there is no one-size-fits-all solution for access control in the IoT. Despite the challenges encountered in designing and implementing the access control in the IoT, it is desired to have an access control solution to meet all the identified requirements to secure the IoT.

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Section: Access Control In the Iotmentioning

confidence: 99%

Section: Access Control Research Challenges and Future Directionsmentioning

confidence: 99%

Access Control for IoT: A Survey of Existing Research, Dynamic Policies and Future Directions

Ragothaman

Wang

Rimal

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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“…As a result, device identification is challenging in the IoT. Many critical services, such as access control and intrusion detection, are built on correctly identifying each unique device [3].…”

Section: Introductionmentioning

confidence: 99%

IoT Device Identification Using Unsupervised Machine Learning

Koball,

Rimal,

Wang

et al. 2023

Information

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Device identification is a fundamental issue in the Internet of Things (IoT). Many critical services, including access control and intrusion prevention, are built on correctly identifying each unique device in a network. However, device identification faces many challenges in the IoT. For example, a common technique to identify a device in a network is using the device’s MAC address. However, MAC addresses can be easily spoofed. On the other hand, IoT devices also include dynamic characteristics such as traffic patterns which could be used for device identification. Machine-learning-assisted approaches are promising for device identification since they can capture dynamic device behaviors and have automation capabilities. Supervised machine-learning-assisted techniques demonstrate high accuracies for device identification. However, they require a large number of labeled datasets, which can be a challenge. On the other hand, unsupervised machine learning can also reach good accuracies without requiring labeled datasets. This paper presents an unsupervised machine-learning approach for IoT device identification.

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“…In recent years, machine learning and deep learning techniques have been applied to enhance ACP recognition; however, current solutions [ 11 ] often suffer from low accuracy and data privacy concerns. This challenge is particularly pronounced in IoT access control [ 12 ], where improving the accuracy of ACP recognition while ensuring the security and privacy of user data is a significant challenge.…”

Section: Introductionmentioning

confidence: 99%

Statement Recognition of Access Control Policies in IoT Networks

Ma,

Yang,

et al. 2023

Sensors

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Access Control Policies (ACPs) are essential for ensuring secure and authorized access to resources in IoT networks. Recognizing these policies involves identifying relevant statements within project documents expressed in natural language. While current research focuses on improving recognition accuracy through algorithm enhancements, the challenge of limited labeled data from individual clients is often overlooked, which impedes the training of highly accurate models. To address this issue and harness the potential of IoT networks, this paper presents FL-Bert-BiLSTM, a novel model that combines federated learning and pre-trained word embedding techniques for access control policy recognition. By leveraging the capabilities of IoT networks, the proposed model enables real-time and distributed training on IoT devices, effectively mitigating the scarcity of labeled data and enhancing accessibility for IoT applications. Additionally, the model incorporates pre-trained word embeddings to leverage the semantic information embedded in textual data, resulting in improved accuracy for access control policy recognition. Experimental results substantiate that the proposed model not only enhances accuracy and generalization capability but also preserves data privacy, making it well-suited for secure and efficient access control in IoT networks.

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A Systematic Mapping Study of Access Control in the Internet of Things

Cited by 3 publications

References 37 publications

Access Control for IoT: A Survey of Existing Research, Dynamic Policies and Future Directions

Access Control for IoT: A Survey of Existing Research, Dynamic Policies and Future Directions

IoT Device Identification Using Unsupervised Machine Learning

Statement Recognition of Access Control Policies in IoT Networks

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