AI-Based Anomaly and Data Posing Classification in Mobile Crowd Sensing

Aysha Alharam, Hadi Otrok, Wael Elmedany, Ahsan Baidar Bakht, Nouf Alkaabi

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

2 Scopus citations

Abstract

Nowadays, Mobile Crowd Sensing (MCS) became the popular paradigm for sensing data. MCS is vulnerable to many types of threats and faces many challenges. Trustworthiness is one of the main MCS challenges; attackers aim to inject faulty data to corrupt the system or waste its resources. Thus, MCS organizers must ensure that no malicious users are contributing to have trusted sensed data. Faulty sensor readings in MCS can be due to sensor failure or malicious behavior. Attackers targets degrade the system performance and reduce the worker's reputation by injecting false data. This paper evaluates different machine learning algorithms classifying the received sensed data as true, a faulty sensor, or attacker behavior. These algorithms are Decision Tree (DT), Support Vector Machine (SVM), and Random Frost (RF). Evaluating the result for comparison obtained based on accuracy, precision, Recall, f1 score, and the confusion matrix. The result shows that among all classifiers, RF shows the highest accuracy of 97.9%.

Original languageBritish English
Title of host publication2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages225-229
Number of pages5
ISBN (Electronic)9781665440325
DOIs
StatePublished - 29 Sep 2021
Event2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021 - Virtual, Online, Bahrain
Duration: 29 Sep 202130 Sep 2021

Publication series

Name2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

Conference

Conference2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021
Country/TerritoryBahrain
CityVirtual, Online
Period29/09/2130/09/21

Keywords

  • attacker
  • crowdsensing
  • fault data
  • ML

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