Visible to the public Uncertainty-Aware Authentication Model for Fog Computing in IoT

TitleUncertainty-Aware Authentication Model for Fog Computing in IoT
Publication TypeConference Paper
Year of Publication2019
AuthorsHeydari, Mohammad, Mylonas, Alexios, Katos, Vasilios, Balaguer-Ballester, Emili, Tafreshi, Vahid Heydari Fami, Benkhelifa, Elhadj
Conference Name2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)
KeywordsABAC model, attribute based access control model, authentication, authorisation, Bayes methods, Cisco Systems, cloud computing, Decision Tree, Decision trees, Fog Computing, Fog Computing and Security, Internet of Things, IoT, logistic regression, Mobile Edge Computing, naive Bayes, pattern classification, prediction model, privacy issues, pubcrawl, regression analysis, Resiliency, Scalability, security, security issues, supervised classification algorithms, supervised learning, support vector machine, Support vector machines, Uncertainty, uncertainty handling, uncertainty-aware authentication model

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, Naive Bayes, Logistic Regression and Support Vector Machine. Our model can achieve authentication performance with 88.14% accuracy using Logistic Regression.

Citation Keyheydari_uncertainty-aware_2019