Masterarbeit MSTR-3048

Bibliograph.
Daten
Mahmoud, Ahmed: Machine Learning in Physical Cryptography.
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik, Masterarbeit Nr. 3048 (2010).
63 Seiten, englisch.
CR-Klassif.A.0 (General Literature, General)
Kurzfassung

Abstract Physical cryptography is a new emerging field of cryptography that has been developed in order to provide a cheap, simple and reliable mean of security primitives. Unlike traditional cryptography, physical cryptography does not depend on unproven mathematical assumptions, rather depends on the unique complex structure of physical systems. Some of the devices used for physical cryptography are known as physical unclonable functions (PUFs). The Arbiter PUF was the first electrical PUF to be constructed. It gained a wide-spread acceptance due to its interesting behavior. The arbiter PUF employs the uncontrollable capacitance variation, which is introduced during the manufacturing process, to provide runtime delay variation between two symmetric paths. Consequently, the generated output, depending on the comparison of the runtime delay difference between the paths, would be unique for each arbiter PUF. In this thesis, we clearly elaborated the general idea of PUFs. In addition, we focused on electrical PUFs, especially the well known arbiter based PUFs. We showed that machine learning is the natural cryptanalysis technique for arbiter based PUFs. Afterwards, we demonstrated our study on an important arbiter based PUF; namely, the Feed-forward arbiter PUF. Moreover, we introduced ideas for novel complex arbiter based PUFs and evaluated them through extensive experimentation.

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Abteilung(en)Universität Stuttgart, Institut für Visualisierung und Interaktive Systeme, Visualisierung und Interaktive Systeme
BetreuerFrank Sehnke, Ulrich Rührmair
Eingabedatum17. Februar 2011
   Publ. Informatik