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Books > Computing & IT > Computer communications & networking > Network security
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Statistical Trend Analysis of Physically Unclonable Functions - An Approach via Text Mining (Hardcover)
Loot Price: R1,513
Discovery Miles 15 130
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Statistical Trend Analysis of Physically Unclonable Functions - An Approach via Text Mining (Hardcover)
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Physically 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.
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