TY - JOUR
T1 - TONet
T2 - A Fast and Efficient Method for Traffic Obfuscation Using Adversarial Machine Learning
AU - Yang, Fan
AU - Wen, Bingyang
AU - Comaniciu, Cristina
AU - Subbalakshmi, K. P.
AU - Chandramouli, R.
N1 - Publisher Copyright:
© 1997-2012 IEEE.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - In this letter, we address the problem of privacy leakage in communications based on analysis of traffic patterns. We propose an efficient method of traffic obfuscation based on neural networks, that generates traffic distortions with minimal overhead and computational cost. Our experimental results show that the proposed method is orders of magnitude faster in implementation and has a higher obfuscation success rate with less perturbation on the traffic samples, compared to previously proposed adversarial machine learning-based traffic obfuscation methods.
AB - In this letter, we address the problem of privacy leakage in communications based on analysis of traffic patterns. We propose an efficient method of traffic obfuscation based on neural networks, that generates traffic distortions with minimal overhead and computational cost. Our experimental results show that the proposed method is orders of magnitude faster in implementation and has a higher obfuscation success rate with less perturbation on the traffic samples, compared to previously proposed adversarial machine learning-based traffic obfuscation methods.
KW - Traffic type obfuscation
KW - adversarial learning
KW - privacy leakage
KW - security
UR - http://www.scopus.com/inward/record.url?scp=85135737144&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135737144&partnerID=8YFLogxK
U2 - 10.1109/LCOMM.2022.3195685
DO - 10.1109/LCOMM.2022.3195685
M3 - Article
AN - SCOPUS:85135737144
SN - 1089-7798
VL - 26
SP - 2537
EP - 2541
JO - IEEE Communications Letters
JF - IEEE Communications Letters
IS - 11
ER -