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

Reinforcement learning (RL) operating on attack graphs leveraging cyberterrain principles are used to develop reward and state associated withdetermination of surveillance detection routes (SDR). This work extendsprevious efforts on developing RL methods for path analysis within enterprisenetworks. This work focuses on building SDR where the routes focus on exploringthe network services while trying to evade risk. RL is utilized to support thedevelopment of these routes by building a reward mechanism that would help inrealization of these paths. The RL algorithm is modified to have a novelwarm-up phase which decides in the initial exploration which areas of thenetwork are safe to explore based on the rewards and penalty scale factor.

Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain