Proximity Based Mobile Device Theft Protection
2024 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE credits
Student thesis
Abstract [en]
Mobile devices such as Laptops are common targets for opportunistic thefts. The only way to properly protect data on a stolen devices is with full-disk encryption. But should the device be stolen when it is powered on, the disk is in an unencrypted state and the encryption keys themselves reside in the devices’ Random-Access-Memory (RAM). This thesis aim to answer what techniques exists to approximate the distance between two devices using Bluetooth Low Energy (BLE). How a BLE connection can utilize continuous authentication to enable full-disk encryption in case of theft, and how such a continuous authentication scheme can be protected from replay attacks. The work methodology was that of design science. The method first defined global objectives for the final artifact. Then throughout multiple iterations, local objectives to that iteration was also defined. This was followed by designing an artifact that builds on the artifact from the previous iteration, and evaluating the result to the set objectives. Three different techniques were found for distance approximation with Bluetooth Low Energy. Received signal strength indicator (RSSI) which is a common technique but suffers from volatility problems. Angle of Arrival and Angle of Departure which requires dedicated hardware, and finally Channel Sounding which is currently in development by Bluetooth SIG. The final artifact produced used signed and hashed RSSI values from a physical access token. In order to approximate the distance between it and the laptop. With this distance approximation the laptop could instruct itself to hibernate, clearing RAM and encrypting the disk. Replay protection was accomplished by assigning valid packets with a nonce and a time limited lifespan.Â
Place, publisher, year, edition, pages
2024. , p. 69
Keywords [en]
IoT, Bluetooth Low Energy, continuous authentication, proximity authentication, replay attack, Received Signal Strength Indicator
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-130150OAI: oai:DiVA.org:lnu-130150DiVA, id: diva2:1867475
Subject / course
Computer Science
Educational program
Network Security Programme, 180 credits
Supervisors
Examiners
2024-06-252024-06-102024-06-25Bibliographically approved