Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Design and Development Considerations of a Cyber Physical Testbed for Operational Technology Research and Education.

Sensors (Basel, Switzerland)·2024
Same author

Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling.

Sensors (Basel, Switzerland)·2024
Same author

SenseCrypt: A Security Framework for Mobile Crowd Sensing Applications.

Sensors (Basel, Switzerland)·2020
Same author

Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors.

Sensors (Basel, Switzerland)·2018
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing.

Nsikak Owoh1, Jackie Riley1, Moses Ashawa1

  • 1Department of Cyber Security and Networks, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, UK.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive model to detect malicious data in mobile crowdsensing (MCS) systems. The TCN-based model achieves 98% accuracy in identifying and mitigating threats to ensure data integrity.

Keywords:
autoencodersdeep learninginternet of thingsmalicious data detectionmobile crowd sensingtemporal convolutional networks

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

741

Related Experiment Videos

Last Updated: Jun 28, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

741

Area of Science:

  • Computer Science
  • Cybersecurity
  • Data Science

Background:

  • Mobile crowdsensing (MCS) systems collect data from mobile devices, posing security risks due to potential data poisoning.
  • Vulnerabilities in MCS systems can compromise data integrity and reliability.
  • Existing detection methods may struggle against evolving adversarial tactics.

Purpose of the Study:

  • To propose an adaptive and robust model for detecting malicious sensor data in MCS.
  • To enhance the security and trustworthiness of mobile crowdsensing systems.
  • To mitigate the impact of adversarial attacks on collected data.

Main Methods:

  • Developed a Temporal Convolutional Network (TCN)-based model with an adaptive learning mechanism.
  • Incorporated continuous evolution to detect novel malicious data patterns.
  • Evaluated performance using the SherLock datasets for comprehensive analysis.

Main Results:

  • The proposed TCN-based model demonstrated high effectiveness in detecting malicious sensor data.
  • Achieved a detection accuracy score of 98% in performance evaluations.
  • Successfully mitigated potential threats to MCS system integrity.

Conclusions:

  • The adaptive TCN model significantly enhances the security of mobile crowdsensing systems.
  • The model's ability to adapt to evolving threats ensures robust data integrity.
  • This research contributes to developing more reliable and secure crowdsensing applications.