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Uncertainty-aware authentication model for fog computing in IoT.

Heydari, M., Mylonas, A., Katos, V., Balaguer-Ballester, E., Tafreshi, V.H.F. and Benkhelifa, E., 2019. Uncertainty-aware authentication model for fog computing in IoT. In: Fourth International Conference on Fog and Mobile Edge Computing (FMEC), 10-13 June 2019, Rome, Italy, 52 - 59.

Full text available as:

08795332.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1109/FMEC.2019.8795332


Since the term 'Fog Computing' has been coined by Cisco Systems in 2012, security and privacy issues of this promising paradigm are still open challenges. Among various security challenges, Access Control is a crucial concern for all cloud computing-like systems (e.g. Fog computing, Mobile edge computing) in the IoT era. Therefore, assigning the precise level of access in such an inherently scalable, heterogeneous and dynamic environment is not easy to perform. This work defines the uncertainty challenge for authentication phase of the access control in fog computing because on one hand fog has a number of characteristics that amplify uncertainty in authentication and on the other hand applying traditional access control models does not result in a flexible and resilient solution. Therefore, we have proposed a novel prediction model based on the extension of Attribute Based Access Control (ABAC) model. Our data-driven model is able to handle uncertainty in authentication. It is also able to consider the mobility of mobile edge devices in order to handle authentication. In doing so, we have built our model using and comparing four supervised classification algorithms namely as Decision Tree, Naïve Bayes, Logistic Regression and Support Vector Machine. Our model can achieve authentication performance with 88.14% accuracy using Logistic Regression.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Uncertainty Authentication; Fog Computing; Mobile Edge Computing; Internet of Things; Supervised Learning; Prediction Model
Group:Faculty of Science & Technology
ID Code:32765
Deposited By: Symplectic RT2
Deposited On:16 Sep 2019 11:36
Last Modified:14 Mar 2022 14:17


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