In the area of computer and network security, due to the insufficiency, high costs, and user-unfriendliness of existing defending methods against a number of cyber attacks, focus for developing new security improvement methods has shifted from the digital to analog domain. In the analog domain, devices are distinguished based on the present variations and characteristics in their physical signals. In fact, each device has unique features in its signal that can be used for identification and monitoring purposes. In this regard, the term physical layer identification (PLI) or device fingerprinting refers to the process of classifying different electronic devices based on their analog identities that are created by employment of signal processing and data analysis methods. Due to the fact that a device behavior undergoes changes due to variations in external and internal conditions, the available PLI techniques might not be able to identify the device reliably. Therefore, a tracking system that is capable of extracting and explaining the present variations in the electrical signals is required to be developed. In order to achieve the best possible tracking system, a number of prediction models are designed using certain statistical techniques. In order to evaluate the performance of these models, models are run on the acquired data from five different fabrications of the same device in four distinct experiments. The results of performance evaluation show that the surrounding temperature of a device is the best option for predicting its signal. The last part of this research project belongs to the security evaluation of a PLI system. The leveraged security examination technique exposes the PLI system to different types of attacks and evaluates its defending strength accordingly. Based on the mechanism of the employed attack in this work, the forged version of a device’s signal is generated using an arbitrary waveform generator (AWG) and is sent to the PLI system. The outcomes of this experiment indicate that the leveraged PLI technique is strong enough in defeating this attack.
| Identifer | oai:union.ndltd.org:UTAHS/oai:digitalcommons.usu.edu:etd-5580 |
| Date | 01 May 2015 |
| Creators | Taheri, Shayan |
| Publisher | DigitalCommons@USU |
| Source Sets | Utah State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | All Graduate Theses and Dissertations |
| Rights | Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact Andrew Wesolek (andrew.wesolek@usu.edu). |
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