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An R-Based Landscape Validation of a Competing Risk Model
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Dynamic cyber risk estimation with competitive quantile autoregression.

Raisa Dzhamtyrova1,2, Carsten Maple1,3

  • 1The Alan Turing Institute, London, United Kingdom.

Data Mining and Knowledge Discovery
|April 11, 2022
PubMed
Summary
This summary is machine-generated.

Effective cyber risk estimation is crucial. This study introduces Quantile Autoregression (QAR) and Competitive Quantile Autoregression (CQAR) methods to model data value at risk, improving cyber attack prediction.

Keywords:
Competitive predictionCyber breach modellingCyber riskDynamic risk estimationQuantile AutoregressionTime-series

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Area of Science:

  • Cybersecurity
  • Data Science
  • Financial Modeling

Background:

  • Enterprises face increasing cyber threats due to valuable data.
  • Accurate cyber risk estimation is vital for mitigating potential impacts.
  • Existing methods may lack flexibility in dynamic risk assessment.

Purpose of the Study:

  • To propose novel methods for cyber risk estimation using time-series data.
  • To introduce Quantile Autoregression (QAR) and Competitive Quantile Autoregression (CQAR).
  • To enhance the prediction of cyber hacking breach size and frequency.

Main Methods:

  • Utilizing Quantile Autoregression (QAR) for estimating Value-at-Risk (VaR) at various confidence levels.
  • Developing Competitive Quantile Autoregression (CQAR) for dynamic, real-time cyber risk re-estimation.
  • Applying coverage tests to validate the predictive capabilities of the proposed models.

Main Results:

  • The proposed QAR and CQAR methods effectively predict the size and inter-arrival times of cyber hacking breaches.
  • CQAR offers dynamic risk assessment with theoretical guarantees for asymptotic performance.
  • The approaches allow for flexible modeling of separate stochastic processes for different significance levels.

Conclusions:

  • QAR and CQAR provide advanced, flexible tools for cyber risk modeling and estimation.
  • These methods enhance the ability to predict and manage the financial impact of cyber attacks.
  • The study offers reproducible code for practical implementation and further research.