000			04083cam a2200481M 4500
001			9781003167105
003			FlBoTFG
005			20240213122832.0
006			m o d
007			cr |n|||||||||
008			210320s2021 xx o 0|| 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a9781000382501 _q(electronic bk.)
020			_a1000382508 _q(electronic bk.)
020			_a9781003167105 _q(electronic bk.)
020			_a1003167101 _q(electronic bk.)
020			_a9781000384062 _q(electronic bk. : EPUB)
020			_a1000384063 _q(electronic bk. : EPUB)
020			_z036775455X
020			_z9780367754556
035			_a(OCoLC)1242465242
035			_a(OCoLC-P)1242465242
050		4	_aTK7872.P47
072		7	_aCOM _x021030 _2bisacsh
072		7	_aCOM _x018000 _2bisacsh
072		7	_aCOM _x083000 _2bisacsh
072		7	_aUN _2bicssc
082	0	4	_a005.8/2 _223
245	0	0	_aSTATISTICAL TREND ANALYSIS OF PHYSICALLY UNCLONABLE FUNCTIONS _h[electronic resource] : _ban approach via text mining.
260			_a[S.l.] : _bCRC PRESS, _c2021.
300			_a1 online resource
520			_aPhysically Unclonable Functions (PUFs) translate unavoidable variations in certain parameters of materials, waves, or devices into random and unique signals. They have found many applications in the Internet of Things (IoT), authentication systems, FPGA industry, several other areas in communications and related technologies, and many commercial products. Statistical Trend Analysis of Physically Unclonable Functions first presents a review on cryptographic hardware and hardware-assisted cryptography. The review highlights PUF as a mega trend in research on cryptographic hardware design. Afterwards, the authors present a combined survey and research work on PUFs using a systematic approach. As part of the survey aspect, a state-of-the-art analysis is presented as well as a taxonomy on PUFs, a life cycle, and an established ecosystem for the technology. In another part of the survey, the evolutionary history of PUFs is examined, and strategies for further research in this area are suggested. In the research side, this book presents a novel approach for trend analysis that can be applied to any technology or research area. In this method, a text mining tool is used which extracts 1020 keywords from the titles of the sample papers. Then, a classifying tool classifies the keywords into 295 meaningful research topics. The popularity of each topic is then numerically measured and analyzed over the course of time through a statistical analysis on the number of research papers related to the topic as well as the number of their citations. The authors identify the most popular topics in four different domains; over the history of PUFs, during the recent years, in top conferences, and in top journals. The results are used to present an evolution study as well as a trend analysis and develop a roadmap for future research in this area. This method gives an automatic popularity-based statistical trend analysis which eliminates the need for passing personal judgments about the direction of trends, and provides concrete evidence to the future direction of research on PUFs. Another advantage of this method is the possibility of studying a whole lot of existing research works (more than 700 in this book). This book will appeal to researchers in text mining, cryptography, hardware security, and IoT.
588			_aOCLC-licensed vendor bibliographic record.
650		7	_aCOMPUTERS / Database Management / Data Mining _2bisacsh
650		7	_aCOMPUTERS / Data Processing / General _2bisacsh
650		7	_aCOMPUTERS / Cryptography _2bisacsh
650		0	_aBibliometrics _vCase studies.
650		0	_aBibliographical citations _xStatistics _vCase studies.
650		0	_aData mining _vCase studies.
700	1		_aZolfaghari, Behrouz, _eeditor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003167105
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c5943 _d5943