English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

PUFs are a class of security primitives that rely on statistical variations of integrated circuits production processes to provide authentication without the need explicitly storing cryptographic keys. Nevertheless several PUF architectures have shown to be predictable using machine learning techniques. This work extends a code published on related work that maximizes the output entropy for a single PUF architecture to a class of delay based PUF architectures and provides a method to make machine learning attacks more efficient against these devices.

Predicting PUFs faster: using high entropy input codes to improve machine learning attacks on a category of Physical Unclonable Functions