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

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

Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inje...

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Bibliographic Details
Main Authors: Nsikak Owoh, Jackie Riley, Moses Ashawa, Salaheddin Hosseinzadeh, Anand Philip, Jude Osamor
Format: Article
Language:English
Published: MDPI AG 2024-04-01
Series:Sensors
Subjects:
mobile crowd sensing
autoencoders
internet of things
deep learning
temporal convolutional networks
malicious data detection
Online Access:https://www.mdpi.com/1424-8220/24/7/2353

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https://www.mdpi.com/1424-8220/24/7/2353

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