University of Hertfordshire

By the same authors

Uncertainty-aware authentication model for fog computing in IoT

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Mohammad Heydari
  • Alexios Mylonas
  • Vasilios Katos
  • Emili Balaguer-Ballester
  • Vahid Heydari Fami Tafreshi
  • Elhadj Benkhelifa
View graph of relations
Original languageEnglish
Title of host publication2019 4th International Conference on Fog and Mobile Edge Computing, FMEC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages52-59
Number of pages8
ISBN (Electronic)9781728117966
DOIs
Publication statusPublished - 15 Aug 2019
Event4th International Conference on Fog and Mobile Edge Computing, FMEC 2019 - Rome, Italy
Duration: 10 Jun 201913 Jun 2019

Publication series

Name2019 4th International Conference on Fog and Mobile Edge Computing, FMEC 2019

Conference

Conference4th International Conference on Fog and Mobile Edge Computing, FMEC 2019
Country/TerritoryItaly
CityRome
Period10/06/1913/06/19

Abstract

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.

ID: 22839081