Skip to main navigation Skip to search Skip to main content

Collusion attacks in Internet of Things: Detection and mitigation using a fog based model

  • Jordan University of Science and Technology
  • Zayed University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

Abstract

This paper discusses the problem of collusion attacks in Internet of Things (IoT) environments and how mobility of IoT devices increases the difficulty of detecting such types of attacks. It demonstrates how approaches used in detecting collusion attacks in WSNs are not applicable in IoT environments. To this end, the paper introduces a model based on the Fog Computing infrastructure to keep track of IoT devices and detect collusion attackers. The model uses fog computing layer for real-time monitoring and detection of collusion attacks in IoT environments. Moreover, the model uses a software defined system layer to add a degree of flexibility for configuring Fog nodes in order to enable them to detect various types of collusion attacks. Furthermore, the paper highlights the possible overhead on Fog nodes and network when applying the proposed model, and claims that the Fog layer infrastructure can provide the required resources for the scalability of the model.

Original languageEnglish
Title of host publicationSAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509032020
DOIs
StatePublished - 6 Apr 2017
Externally publishedYes
Event12th IEEE Sensors Applications Symposium, SAS 2017 - Glassboro, United States
Duration: 13 Mar 201715 Mar 2017

Publication series

NameSAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings

Conference

Conference12th IEEE Sensors Applications Symposium, SAS 2017
Country/TerritoryUnited States
CityGlassboro
Period13/03/1715/03/17

Fingerprint

Dive into the research topics of 'Collusion attacks in Internet of Things: Detection and mitigation using a fog based model'. Together they form a unique fingerprint.

Cite this