AEGANAuth: Autoencoder GAN-Based Continuous Authentication With Conditional Variational Autoencoder Generative Adversarial Network

In recent years, sensor-based continuous authentication on mobile devices has proven highly effective in safeguarding personal information. However, these proposed approaches often require the utilization of both legitimate user and imposters' data for training authentication models, which is t...

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Vydáno v:IEEE internet of things journal Ročník 11; číslo 16; s. 27635 - 27650
Hlavní autoři: Li, Yantao, Ouyang, Caike, Huang, Hongyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 15.08.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accelerometers
Authentication
Autoencoder GAN (AEGAN)
Coders
conditional variational AEGAN (CVAEGAN)
continuous authentication
Data augmentation
Discriminators
Electronic devices
equal error rate (EER)
Errors
Generative adversarial networks
Gyroscopes
Reconstruction
Security
Sensors
Training
ISSN:2327-4662, 2327-4662
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Shrnutí:In recent years, sensor-based continuous authentication on mobile devices has proven highly effective in safeguarding personal information. However, these proposed approaches often require the utilization of both legitimate user and imposters' data for training authentication models, which is time consuming and ineffective. In this article, we present AEGANAuth, a lightweight and effective autoencoder GAN (AEGAN)-based continuous Authentication system for mobile devices using conditional variational autoencoder generative adversarial network (CVAEGAN). AEGANAuth uses a CVAEGAN for data augmentation and utilizes an AEGAN for user data reconstruction. During the enrollment phase, AEGANAuth employs the accelerometer and gyroscope sensors embedded on mobile devices to implicitly collect user behavioral patterns. Using the normalized sensor data, AEGANAuth selects legitimate user data to train CVAEGAN, which consists of a variational encoder, a conditional generator, a discriminator, and a classifier, for AEGAN training data augmentation. Based on the augmented legitimate user data, AEGAN, comprising an encoder, a decoder, and a discriminator, is trained for user data reconstruction. In the authentication phase, when a user operates the mobile device, AEGANAuth collects and normalizes the current user's data, and then employs the trained AEGAN to reconstruct this user's data. The reconstruction error is then computed by comparing the reconstructed data to the normalized data. Finally, AEGANAuth with AEGAN compares the reconstruction error to a predetermined authentication threshold for user authentication. We evaluate the performance of AEGANAuth on our data set, and the experimental results demonstrate an average equal error rate (EER) of 2.13% and an average accuracy of 97.85% on ten imposters.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3399549