An increasing number of devices, from coffee makers to electric kettles, are becoming connected to the Internet. These are all a part of the Internet of Things, or IoT. Each device generates unique network traffic and power consumption patterns. Until now, there has not been a comprehensive set of data that captures these traffic and power patterns. This thesis documents how we collected 10 to 15 weeks of network traffic and power consumption data from 15 different IoT devices and provides an analysis of a subset of 6 devices. Devices including an Amazon Echo Dot, Google Home Mini, and Google Chromecast were used on a regular basis and all of their network traffic and power consumption was logged to a MySQL database. The database currently contains 64 million packets and 71 gigabytes of data and is still growing in size as more data is collected 24/7 from each device. We show that it is possible to see when users are asking their smart speaker a question or whether the lights in their home are on or off based on power consumption and network traffic from the devices. These trends can be seen even if the data being sent is encrypted.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3196 |
| Date | 01 June 2018 |
| Creators | Frawley, Ryan Joseph |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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