A Deep Learning Method for the Security Vulnerability Study of Feed-Forward Physical Unclonable Functions

Authentication is critical for Internet-of-Things. The traditional approach of using cryptographic keys is subject to invasive attacks. Being unclonable even by the manufacturers, physical unclonable functions (PUFs) leverage integrated circuits’ manufacturing variations to produce responses unique...

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Veröffentlicht in:	Arabian journal for science and engineering (2011) Jg. 49; H. 9; S. 12291 - 12303
Hauptverfasser:	Alkatheiri, Mohammed Saeed, Aseeri, Ahmad O., Zhuang, Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024 Springer Nature B.V
Schlagworte:	Cybersecurity Deep learning Engineering Humanities and Social Sciences Integrated circuits Internet of Things Machine learning Malware multidisciplinary Neural networks Research Article-Computer Engineering and Computer Science Science Deep learning IoT security Feed-forward Arbiter PUF Arbiter PUF Neural networks
ISSN:	2193-567X, 1319-8025, 2191-4281
Online-Zugang:	Volltext
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