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

In this work, we investigate the effectiveness of deep-learning-basedpassword guessing models for targeted attacks on human-chosen passwords. Inrecent years, service providers have increased the level of security ofusers'passwords. This is done by requiring more complex password generationpatterns and by using computationally expensive hash functions. For theattackers this means a reduced number of available guessing attempts, whichintroduces the necessity to target their guess by exploiting a victim'spublicly available information. In this work, we introduce a context-awarepassword guessing model that better capture attackers'behavior. We demonstratethat knowing a victim's email address is already critical in compromising theassociated password and provide an in-depth analysis of the relationshipbetween them. We also show the potential of such models to identify clusters ofusers based on their password generation behaviour, which can spot fakeprofiles and populations more vulnerable to context-aware guesses. The code ispublicly available at https://github.com/spring-epfl/DCM_sp

Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning