Peek-a-boo: i see your smart home activities, even encrypted!

By Abbas Acar, Hossein Fereidooni, Tigist Abera, A. Sikder, Markus Miettinen, Hidayet Aksu, Mauro Conti, Ahmad-Reza Sadeghi, Selcuk Uluagac. Wireless Network Security, 2018

318 citations recorded by the source corpus.

A myriad of IoT devices such as bulbs, switches, speakers in a smart home environment allow users to easily control the physical world around them and facilitate their living styles through the sensors already embedded in these devices. Sensor data contains a lot of sensitive information about the user and devices. However, an attacker inside or near a smart home environment can potentially exploit the innate wireless medium used by these devices to exfiltrate sensitive information from the encrypted payload (i.e., sensor data) about the users and their activities, invading user privacy. With this in mind, in this work, we introduce a novel multi-stage privacy attack against user privacy in a smart environment. It is realized utilizing state-of-the-art machine-learning approaches for detecting and identifying the types of IoT devices, their states, and ongoing user activities in a cascading style by only passively sniffing the network traffic from smart home devices and sensors. The attack effectively works on both encrypted and unencrypted communications. We evaluate the efficiency of the attack with real measurements from an extensive set of popular off-the-shelf smart home IoT devices utilizing a set of diverse network protocols like WiFi, ZigBee, and BLE. Our results show that an adversary passively sniffing the traffic can achieve very high accuracy (above 90%) in identifying the state and actions of targeted smart home devices and their users. To protect against this privacy leakage, we also propose a countermeasure based on generating spoofed traffic to hide the device states and demonstrate that it provides better protection than existing solutions.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f660a4253bfd8fb4f67a95e867225013282a2aba

How researchers see this paper

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

In Artificial Intelligence

In Computer Networks and Communications

In Signal Processing

Martina Lindorfer

Citations in the corpus

FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic (2020, background): “Various fingerprinting approaches rely on repetitive behavior in network traffic [1, 28].”
Information Exposure From Consumer IoT Devices: A Multidimensional, Network-Informed Measurement Approach (2019, unknown): “Other approaches for detecting unexpected behavior use machine learning and other statistical techniques on the device traffic to infer the type of the IoT devices and their activities, and to profile their users [2, 41, 44].”