A Perfect Match: Deep Learning Towards Enhanced Data Trustworthiness in Crowd-Sensing Systems

Sam Afzal-Houshmand, Sajad Homayoun, Thanassis Giannetsos

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

The advent of IoT edge devices has enabled the collection of rich datasets, as part of Mobile Crowd Sensing (MCS), which has emerged as a key enabler for a wide gamut of safetycritical applications ranging from traffic control, environmental monitoring to assistive healthcare. Despite the clear advantages that such unprecedented quantity of data brings forth, it is also subject to inherent data trustworthiness challenges due to factors such as malevolent input and faulty sensors. Compounding this issue, there has been a plethora of proposed solutions, based on the use of traditional machine learning algorithms, towards assessing and sifting faulty data without any assumption on the trustworthiness of their source. However, there are still a number of open issues: how to cope with the presence of strong, colluding adversaries while at the same time efficiently managing this high influx of incoming user data. In this work, we meet these challenges by proposing the hybrid use of Deep Learning schemes (i.e., LSTMs) and conventional Machine Learning classifiers (i.e. One-Class Classifiers) for detecting and filtering out false data points. We provide a prototype implementation coupled with a detailed performance evaluation under various (attack) scenarios, employing both real and synthetic datasets. Our results showcase how the proposed solution outperforms various existing resilient aggregation and outlier detection schemes.
Original languageEnglish
Title of host publicationProceedings of IEEE International Mediterranean Conference on Communications and Networking
Number of pages7
PublisherIEEE
Publication date2021
ISBN (Print)978-1-6654-4506-1
DOIs
Publication statusPublished - 2021
EventIEEE International Mediterranean Conference on Communications and Networking - Electra Palace Hotel, Athens, Greece
Duration: 7 Sep 202110 Sep 2021
https://meditcom2021.ieee-meditcom.org/

Conference

ConferenceIEEE International Mediterranean Conference on Communications and Networking
LocationElectra Palace Hotel
Country/TerritoryGreece
CityAthens
Period07/09/202110/09/2021
Internet address

Keywords

  • Mobile Crowd Sensing
  • Adversarial Machine Learning
  • Data Trustworthiness
  • LSTM
  • One-Class Classifier

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