# Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

arXiv: https://arxiv.org/abs/1909.00104 · HTML: https://mlsanity.com/paper/1909.00104/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation](https://mlsanity.com/paper/2403.05822/) (2024)
- [Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study](https://mlsanity.com/paper/2203.09332/) (2022)
- [ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classification](https://mlsanity.com/paper/2310.08036/) (2023)
- [UniNet: A Unified Multi-granular Traffic Modeling Framework for Network Security](https://mlsanity.com/paper/2503.04174/) (2025)
- [Locality Sensitive Hashing for Network Traffic Fingerprinting](https://mlsanity.com/paper/2402.08063/) (2024)
- [Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey](https://mlsanity.com/paper/2403.16149/) (2024)

## Abstract (arXiv)

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue.