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The IoT (Internet of Things) technology has been widely adopted in recentyears and has profoundly changed the people's daily lives. However, in themeantime, such a fast-growing technology has also introduced new privacyissues, which need to be better understood and measured. In this work, we lookinto how private information can be leaked from network traffic generated inthe smart home network. Although researchers have proposed techniques to inferIoT device types or user behaviors under clean experiment setup, theeffectiveness of such approaches become questionable in the complex butrealistic network environment, where common techniques like Network Address andPort Translation (NAPT) and Virtual Private Network (VPN) are enabled. Trafficanalysis using traditional methods (e.g., through classical machine-learningmodels) is much less effective under those settings, as the features pickedmanually are not distinctive any more. In this work, we propose a trafficanalysis framework based on sequence-learning techniques like LSTM andleveraged the temporal relations between packets for the attack of deviceidentification. We evaluated it under different environment settings (e.g.,pure-IoT and noisy environment with multiple non-IoT devices). The resultsshowed our framework was able to differentiate device types with a highaccuracy. This result suggests IoT network communications pose prominentchallenges to users' privacy, even when they are protected by encryption andmorphed by the network gateway. As such, new privacy protection methods on IoTtraffic need to be developed towards mitigating this new issue.

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