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

The increasing value of data held in enterprises makes it an attractivetarget to attackers. The increasing likelihood and impact of a cyber attackhave highlighted the importance of effective cyber risk estimation. We proposetwo methods for modelling Value-at-Risk (VaR) which can be used for anytime-series data. The first approach is based on Quantile Autoregression (QAR),which can estimate VaR for different quantiles, i.e. confidence levels. Thesecond method, we term Competitive Quantile Autoregression (CQAR), dynamicallyre-estimates cyber risk as soon as new data becomes available. This methodprovides a theoretical guarantee that it asymptotically performs as well as anyQAR at any time point in the future. We show that these methods can predict thesize and inter-arrival time of cyber hacking breaches by running coveragetests. The proposed approaches allow to model a separate stochastic process foreach significance level and therefore provide more flexibility compared topreviously proposed techniques. We provide a fully reproducible code used forconducting the experiments.

Dynamic cyber risk estimation with Competitive Quantile Autoregression