Version 1
: Received: 4 July 2024 / Approved: 5 July 2024 / Online: 8 July 2024 (02:56:47 CEST)
How to cite:
Xin, Q.; Xu, Z.; Guo, L.; Zhao, F.; Wu, B. IoT Traffic Classification and Anomaly Detection Method based on Deep Autoencoders. Preprints2024, 2024070530. https://doi.org/10.20944/preprints202407.0530.v1
Xin, Q.; Xu, Z.; Guo, L.; Zhao, F.; Wu, B. IoT Traffic Classification and Anomaly Detection Method based on Deep Autoencoders. Preprints 2024, 2024070530. https://doi.org/10.20944/preprints202407.0530.v1
Xin, Q.; Xu, Z.; Guo, L.; Zhao, F.; Wu, B. IoT Traffic Classification and Anomaly Detection Method based on Deep Autoencoders. Preprints2024, 2024070530. https://doi.org/10.20944/preprints202407.0530.v1
APA Style
Xin, Q., Xu, Z., Guo, L., Zhao, F., & Wu, B. (2024). IoT Traffic Classification and Anomaly Detection Method based on Deep Autoencoders. Preprints. https://doi.org/10.20944/preprints202407.0530.v1
Chicago/Turabian Style
Xin, Q., Fanyi Zhao and Binbin Wu. 2024 "IoT Traffic Classification and Anomaly Detection Method based on Deep Autoencoders" Preprints. https://doi.org/10.20944/preprints202407.0530.v1
Abstract
This study investigates anomaly detection of IoT device traffic using Convolutional Neural Networks (CNN) and Variational Autoencoders (VAE) to enhance the detection capability of security threats in IoT environments. A series of hardware configurations, software environments, and hyperparameters were utilized to optimize the training and testing processes of the models. The CNN model demonstrates robust classification performance, achieving an accuracy rate of 95.85% on the test dataset, effectively distinguishing between different types of IoT device traffic. Meanwhile, the VAE model exhibits proficient anomaly detection capabilities by effectively capturing abnormal patterns in the data using reconstruction loss and KL divergence. The combined use of CNN and VAE models offers a comprehensive solution to cybersecurity challenges in IoT environments. Future research directions include exploring diverse IoT traffic data, practical deployment for validation, and further optimization of model structures and parameters to improve performance and applicability.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
