Adaptive Encrypted Traffic Fingerprinting With Bi-Directional Dependence

Recently network traffic analysis has been increasingly used in various applications including security, targeted advertisements, and network management. However, data encryption performed on network traffic poses a challenge to these analysis techniques.
In this paper, we present a novel method to extract characteristics from encrypted traffic by utilizing data dependencies that occur over
sequential transmissions of network packets.
Furthermore, we explore the temporal nature of encrypted traffic and introduce an adaptive model that considers changes in data content over time. We evaluate our analysis on two packet encrypted applications: website fingerprinting and mobile application (app) fingerprinting. Our evaluation shows how the proposed approach outperforms previous works.

Author(s):

Khaled Al-Naami    
The University of Texas at Dallas
United States

Swarup Chandra    
The University of Texas at Dallas
United States

Ahmad Mustafa    
The University of Texas at Dallas
United States

Latifur Khan    
The University of Texas at Dallas
United States

Zhiqiang Lin    
The University of Texas at Dallas
United States

Kevin Hamlen    
The University of Texas at Dallas
United States

Bhavani Thuraisingham    
The University of Texas at Dallas
United States