AppScanner: Automatic Fingerprinting of Smartphone Apps from Encrypted Network Traffic

By Vincent F. Taylor, Riccardo Spolaor, Mauro Conti, I. Martinovic. European Symposium on Security and Privacy, 2016

335 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Robust Smartphone App Identification via Encrypted Network Traffic Analysis (2017, methodology): “In this paper we extend AppScanner, ﬁrst presented by the authors in [8], along several important dimensions.”
Accurate Decentralized Application Identification via Encrypted Traffic Analysis Using Graph Neural Networks (2021, methodology): “[27] proposed Appscanner that combined statistical features of packet length”
Peek-a-boo: i see your smart home activities, even encrypted! (2018, background): “[36] presented their work the fingerprinting of apps from an encrypted network of the smartphone.”
Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges (2019, methodology): “For completeness, the framework included a baseline “shallow” network to assess DL (classification) performance gain and a state-of-the-art ML-based classifier [13], using handcrafted and flow-based features.”
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic (2020, methodology): “Here, we compare our approach against AppScanner [62], a supervised state-of-theart technique to recognize apps in network traffic.”
FS-Net: A Flow Sequence Network For Encrypted Traffic Classification (2019, methodology): “The general procedures include feature engineering and model training [5]–[8], [13].”