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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a security verification framework for cryptographic protocolsusing machine learning. In recent years, as cryptographic protocols have becomemore complex, research on automatic verification techniques has been focusedon. The main technique is formal verification. However, the formal verificationhas two problems: it requires a large amount of computational time and does notguarantee decidability. We propose a method that allows security verificationwith computational time on the order of linear with respect to the size of theprotocol using machine learning. In training machine learning models forsecurity verification of cryptographic protocols, a sufficient amount of data,i.e., a set of protocol data with security labels, is difficult to collect fromacademic papers and other sources. To overcome this issue, we propose a way tocreate arbitrarily large datasets by automatically generating random protocolsand assigning security labels to them using formal verification tools.Furthermore, to exploit structural features of protocols, we construct a neuralnetwork that processes a protocol along its series and tree structures. Weevaluate the proposed method by applying it to verification of practicalcryptographic protocols.

A Security Verification Framework of Cryptographic Protocols Using Machine Learning