As a solution, we propose FlowPrint, a semi-supervised approach for fingerprinting mobile apps from (encrypted) network traffic.
We automatically find temporal correlations among destination-related features of network traffic and use these correlations to generate app fingerprints.
Our approach is 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%, significantly outperforming state-of-the-art solutions.
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.
|Title of host publication||Network and Distributed System Security Symposium (NDSS)|
|Place of Publication||San Diego|
|Publication status||Published - 24 Feb 2020|
|Event||Network and Distributed System Security Symposium, NDSS 2020 - Catamaran Resort Hotel & Spa, San Diego, United States|
Duration: 23 Feb 2020 → 26 Feb 2020
|Conference||Network and Distributed System Security Symposium, NDSS 2020|
|Abbreviated title||NDSS 2020|
|Period||23/02/20 → 26/02/20|