# Robust Smartphone App Identification Via Encrypted Network Traffic Analysis

arXiv: https://arxiv.org/abs/1704.06099 · HTML: https://mlsanity.com/paper/1704.06099/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2017.

## Built on by (most cited first, 16 of 16)

- [ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification](https://mlsanity.com/paper/2202.06335/) (2022)
- [Fine-grained TLS services classification with reject option](https://mlsanity.com/paper/2202.11984/) (2022)
- [On the Local Cache Update Rules in Streaming Federated Learning](https://mlsanity.com/paper/2303.16340/) (2023)
- [Lens: A Knowledge-Guided Foundation Model for Network Traffic](https://mlsanity.com/paper/2402.03646/) (2024)
- [Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification](https://mlsanity.com/paper/2505.20866/) (2025)
- [DataZoo: Streamlining Traffic Classification Experiments](https://mlsanity.com/paper/2310.19568/) (2023)
- [Deep Learning for Encrypted Traffic Classification and Unknown Data Detection](https://mlsanity.com/paper/2203.15501/) (2022)
- [Open-Source Framework for Encrypted Internet and Malicious Traffic Classification](https://mlsanity.com/paper/2206.10144/) (2022)
- [A Testbed for Automating and Analysing Mobile Devices and their Applications](https://mlsanity.com/paper/2309.08158/) (2023)
- [Efficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classification](https://mlsanity.com/paper/2407.19687/) (2024)
- [Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning](https://mlsanity.com/paper/2506.03207/) (2025)
- [PACC: Protocol-Aware Cross-Layer Compression for Compact Network Traffic Representation](https://mlsanity.com/paper/2602.08331/) (2026)
- [CBR -- Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution](https://mlsanity.com/paper/2403.11206/) (2024)
- [Building Transparency in Deep Learning-Powered Network Traffic Classification: A Traffic-Explainer Framework](https://mlsanity.com/paper/2509.18007/) (2025)
- [MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretraining](https://mlsanity.com/paper/2605.11034/) (2026)
- [Traffic-CBM: A Structurally Interpretable Multimodal Framework for Encrypted Traffic Classification](https://mlsanity.com/paper/2606.29909/) (2026)

## Abstract (arXiv)

The apps installed on a smartphone can reveal much information about a user, such as their medical conditions, sexual orientation, or religious beliefs. Additionally, the presence or absence of particular apps on a smartphone can inform an adversary who is intent on attacking the device. In this paper, we show that a passive eavesdropper can feasibly identify smartphone apps by fingerprinting the network traffic that they send. Although SSL/TLS hides the payload of packets, side-channel data such as packet size and direction is still leaked from encrypted connections. We use machine learning techniques to identify smartphone apps from this side-channel data. In addition to merely fingerprinting and identifying smartphone apps, we investigate how app fingerprints change over time, across devices and across different versions of apps. Additionally, we introduce strategies that enable our app classification system to identify and mitigate the effect of ambiguous traffic, i.e., traffic in common among apps such as advertisement traffic. We fully implemented a framework to fingerprint apps and ran a thorough set of experiments to assess its performance. We fingerprinted 110 of the most popular apps in the Google Play Store and were able to identify them six months later with up to 96% accuracy. Additionally, we show that app fingerprints persist to varying extents across devices and app versions.