Skip to Main content Skip to Navigation
Conference papers

Denoising Adversarial Autoencoder for Obfuscated Traffic Detection and Recovery

Abstract : Traffic classification is key for managing both QoS and security in the Internet of Things (IoT). However, new traffic obfuscation techniques have been developed to thwart classification. Traffic mutation is one such obfuscation technique, that consists of modifying the flow’s statistical characteristics to mislead the traffic classifier. In fact, this same technique can also be used to hide normal traffic characteristics for the sake of privacy. However, the concern is its use by attackers to bypass intrusion detection systems by modifying the attack traffic characteristics. In this paper, we propose an unsupervised Deep Learning (DL)-based model to detect mutated traffic. This model is based on generative DL architectures, namely Autoencoders (AE) and Generative Adversarial Network (GAN). This model consists of a denoising AE to de-anonymize the mutated traffic and a discriminator to detect it. The implementation results show that the traffic can be denoised when different mutation techniques are applied with a reconstruction error less than $$10^{-1}$$. In addition, the detection rate of fake traffic reaches 83.7%.
Complete list of metadata
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Monday, June 21, 2021 - 5:31:16 PM
Last modification on : Friday, July 30, 2021 - 2:44:50 PM
Long-term archiving on: : Wednesday, September 22, 2021 - 7:00:55 PM


 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-01-01

Please log in to resquest access to the document


Distributed under a Creative Commons Attribution 4.0 International License



Ola Salman, Imad H. Elhajj, Ayman Kayssi, Ali Chehab. Denoising Adversarial Autoencoder for Obfuscated Traffic Detection and Recovery. 2nd International Conference on Machine Learning for Networking (MLN), Dec 2019, Paris, France. pp.99-116, ⟨10.1007/978-3-030-45778-5_8⟩. ⟨hal-03266453⟩



Record views