Abstract-As smart home devices are introduced into our homes, security and privacy concerns are being raised. Smart home devices collect, exchange, and transmit various data about the environment of our homes. This data can not only be used to characterize a physical property but also to infer personal information about the inhabitants. One potential attack vector for smart home devices is the use of traffic classification as a source for covert channel attacks. Specifically, we are concerned with the use of traffic classification techniques for inferring events taking place within a building. In this work, we study two of the most popular smart home devices, the Nest Thermostat and the wired Nest Protect (i.e. smoke and carbon dioxide detector) and show that traffic analysis can be used to learn potentially sensitive information about the state of a smart home. Among other observations, we show that we can determine, with 88% and 67% accuracy respectively, when the thermostat transitions between the Home and Auto Away mode and vice versa, based only on network traffic originating from the device. This information may be used, for example, by an attacker to infer whether the home is occupied.
The research in Reference REF concerned with the use of traffic classification techniques for inferring events taking place within a building, in order to improve security and privacy concerns of the smart homes.
Is Anybody Home? Inferring Activity From Smart Home Network Traffic
2016 IEEE Security and Privacy Workshops (SPW)
2016 IEEE Security and Privacy Workshops (SPW)
Computer Science Engineering