Thijs van Ede (University of Twente), Riccardo Bortolameotti (Bitdefender), Andrea Continella (UC Santa Barbara), Jingjing Ren (Northeastern University), Daniel J. Dubois (Northeastern University), Martina Lindorfer (TU Wien), David Choffnes (Northeastern University), Maarten van Steen (University of Twente), Andreas Peter (University of Twente)

Mobile-application fingerprinting of network traffic is a valuable tool for many security solutions as it provides insights into the apps active on a network.
Unfortunately, existing techniques require prior knowledge of apps to be able to recognize them.
However, mobile environments are constantly evolving, i.e., apps are regularly installed, updated, and uninstalled.
Therefore, it is infeasible for existing fingerprinting approaches to cover all apps that may appear on a network.
Moreover, most mobile traffic is encrypted, shows similarities with other apps, e.g., due to common libraries or the use of content delivery networks, and depends on user input, further complicating the fingerprinting process.

As a solution, we propose FlowPrint, an unsupervised approach for creating mobile app fingerprints from (encrypted) network traffic.
We automatically find temporal correlations among destination-related features of network traffic and use these correlations to generate app fingerprints.
As this approach is unsupervised, we are able to fingerprint previously unseen apps, something that existing techniques fail to achieve.
We evaluate our approach for both Android and iOS in the setting of app recognition where we achieve an accuracy of 89.2%, outperforming state-of-the-art solutions by 39.0%.
In addition, we show that our approach can detect previously unseen apps with a precision of 93.5%, detecting 72.3% of apps within the first five minutes of communication.

View More Papers

Finding Safety in Numbers with Secure Allegation Escrows

Venkat Arun (Massachusetts Institute of Technology), Aniket Kate (Purdue University), Deepak Garg (Max Planck Institute for Software Systems), Peter Druschel (Max Planck Institute for Software Systems), Bobby Bhattacharjee (University of Maryland)

Read More

DESENSITIZATION: Privacy-Aware and Attack-Preserving Crash Report

Ren Ding (Georgia Institute of Technology), Hong Hu (Georgia Institute of Technology), Wen Xu (Georgia Institute of Technology), Taesoo Kim (Georgia Institute of Technology)

Read More

Unicorn: Runtime Provenance-Based Detector for Advanced Persistent Threats

Xueyuan Han (Harvard University), Thomas Pasquier (University of Bristol), Adam Bates (University of Illinois at Urbana-Champaign), James Mickens (Harvard University), Margo Seltzer (University of British Columbia)

Read More

Learning-based Practical Smartphone Eavesdropping with Built-in Accelerometer

Zhongjie Ba (Zhejiang University and McGill University), Tianhang Zheng (University of Toronto), Xinyu Zhang (Zhejiang University), Zhan Qin (Zhejiang University), Baochun Li (University of Toronto), Xue Liu (McGill University), Kui Ren (Zhejiang University)

Read More