Towards Indeterminacy-Tolerant Access Control in IoT

Heydari, Mohammad; Mylonas, Alexios; Katos, Vasileios; Gritzalis, Dimitris

doi:10.1007/978-3-030-10543-3_4

Cited by 7 publications

(12 citation statements)

References 56 publications

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“…In relation to access control, a number of characteristics have been discussed in the literature, such as delegation, revocation, granularity, flexibility, scalability, low weight, heterogeneity and context-awareness [5]. According to the inherent characteristics of IoT, an access control system can be evaluated by the following criteria [6]:…”

Section: Access Control In Iotmentioning

confidence: 99%

“…According to the above-mentioned criteria for evaluating access control systems, neither traditional nor emerging access control systems are applicable to IoT. Table 1 shows the performance of the access control models against four evaluation criteria in detail [6]. 2 summarized a number of proposed access control models that are based on an extension of the models listed in Table 1.…”

Section: A Access Control Modelsmentioning

confidence: 99%

“…The aforementioned criteria will be used to evaluate whether the following protocols fit IoT: i) Open Authorization (OAuth), 2 an open protocol used to establish a secure authorization over the Web; ii) OpenID, 3 a Web-oriented single sign-on protocol that is widely used by well-known companies such as PayPal and Amazon; iii) Security Assertion Markup Language (SAML), an XML-oriented and open protocol to exchange user authentication and authorization data among security domains; iv) Remote Authentication Dial-in User Service (RADIUS), 4 an authentication network protocol that works in client/server network architecture to provide centralized access to networks (RFC 6929); v) Lightweight Directory Access Protocol (LDAP), 5 which is a centralized and remote authentication network protocol used for authentication and authorization; and vi) Kerberos, 6 a network authentication protocol developed by MIT to provide access to university resources in the 1980s by authenticating clients to services in a distributed system. RADIUS, LDAP and Kerberos are widely used in active directory and database access for access control.…”

Section: A Access Control Modelsmentioning

confidence: 99%

“…• In order to measure the value of the risk, in the literature a number of metrics are suggested, including object sensitivity, the severity of the requested action and the benefit of the access [37], [38]. Moreover, five different methods were suggested for calculating the total value of the risk, and these were discussed in [6]. These classes take into consideration different parameters such as the likelihood of incident, the likelihood of threat, and the impact of the threat or incident.…”

Section: Risk-aware Access Control (Raac)mentioning

confidence: 99%

“…As a result, uncertainty in authentication needs to be defined. As we previously defined it [6], uncertainty in authentication can be defined as a state in which access decisions have to be made based on incomplete information. There are a number of domains in which such a resilient method can be applied, such as vehicle-to-vehicle communication (VANET), virtual organization (VO) in smart grids, and resource sharing (e.g., traffic information) in smart cities.…”

Section: A Uncertaintymentioning

confidence: 99%

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Known unknowns: Indeterminacy in authentication in IoT

Heydari

Mylonas

Benkhelifa

et al. 2020

Future Generation Computer Systems

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The Internet of Things (IoT), comprising a plethora of heterogeneous devices, is an enabling technology that can improve the quality of our daily lives, for instance by measuring parameters from the environment (e.g., humidity, temperature, weather, energy consumption, traffic, and others) or our bodies (e.g., health data). However, as with any technology, IoT has introduced a number of security and privacy challenges. Indeed, IoT devices create, process, transfer and store data, which are often sensitive, and which must be protected from unauthorized access. Similarly, the infrastructure that links with IoT, as well as the IoT devices themselves, is an asset that needs to be protected. The focus of this work is examining authentication in IoT. In particular, in this work we conducted a state-of-theart review of the access control models that have been proposed, including both traditional access control models and emerging models that have recently been proposed and are tailored for IoT. We identified that the existing models cannot cope with indeterminacy, an inherent characteristic of IoT, which hinders authentication decisions. In this context, we studied the two known components of indeterminacy, i.e., uncertainty and ambiguity, and proposed a new model that handles indeterminacy in authentication in IoT environments.

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Section: Access Control In Iotmentioning

confidence: 99%

Section: A Access Control Modelsmentioning

confidence: 99%

Section: A Access Control Modelsmentioning

confidence: 99%

Section: Risk-aware Access Control (Raac)mentioning

confidence: 99%

Section: A Uncertaintymentioning

confidence: 99%

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Known unknowns: Indeterminacy in authentication in IoT

Heydari

Mylonas

Benkhelifa

et al. 2020

Future Generation Computer Systems

Self Cite

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Uncertainty-Aware Authentication Model for IoT

et al. 2020

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Corn Yield Prediction Using Crop Growth and Machine Learning Models

Moswa,

Killeen,

Kiringa

et al. 2023

Lecture Notes in Networks and Systems

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Undoubtedly, the advancement of IoT technology has created a plethora of new applications and a growing number of devices connected to the internet. Among these developments emerged the novel concept of smart farming. In this context, sensor nodes are used in farms to help farmers acquire deeper insight into the environmental factors affecting their productivity.In recent years, we have witnessed an emerging trend of scholarly literature focused on smart farming. Some focus has been on system architecture for monitoring purposes, while another area of interest includes yield prediction. Humidity, air and soil temperature, solar radiation, and wind speed are some key weather elements monitored in smart farms.We introduce a mechanistic crop growth model to predict crop growth and subsequent yield, subject to weather, soil parameters, crop characteristics and management practices. We also seek to measure the influence of nitrogen on yield throughout the growing season. The machine learning models are trained to emulate the crop growth model in the state of Iowa (US). The multilayer perceptron (MLP) is chosen to evaluate the model prediction as it generates fewer errors.Furthermore, the MLP optimization model is used to maximize corn yield. The experiment was performed using different scenarios, stochastic gradient descent (SGD), and adaptive moment estimation (Adam) optimizers. The experiment results revealed that the SGD optimizer and the dataset with the scenario of unchanged parameters provided the highest crop yield compared to the mechanistic crop growth model.

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Towards Indeterminacy-Tolerant Access Control in IoT

Cited by 7 publications

References 56 publications

Known unknowns: Indeterminacy in authentication in IoT

Known unknowns: Indeterminacy in authentication in IoT

Uncertainty-Aware Authentication Model for IoT

Corn Yield Prediction Using Crop Growth and Machine Learning Models

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