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

Cybersecurity is increasingly threatened by advanced and persistent attacks.As these attacks are often designed to disable a system (or a criticalresource, e.g., a user account) repeatedly, it is crucial for the defender tokeep updating its security measures to strike a balance between the risk ofbeing compromised and the cost of security updates. Moreover, these decisionsoften need to be made with limited and delayed feedback due to the stealthynature of advanced attacks. In addition to targeted attacks, such an optimaltiming policy under incomplete information has broad applications incybersecurity. Examples include key rotation, password change, application ofpatches, and virtual machine refreshing. However, rigorous studies of optimaltiming are rare. Further, existing solutions typically rely on a pre-definedattack model that is known to the defender, which is often not the case inpractice. In this work, we make an initial effort towards achieving optimaltiming of security updates in the face of unknown stealthy attacks. We considera variant of the influential FlipIt game model with asymmetric feedback andunknown attack time distribution, which provides a general model to consecutivesecurity updates. The defender's problem is then modeled as a time associativebandit problem with dependent arms. We derive upper confidence bound basedlearning policies that achieve low regret compared with optimal periodicdefense strategies that can only be derived when attack time distributions areknown.

When to Reset Your Keys: Optimal Timing of Security Updates via Learning