Modeling multivariate cyber risks: deep learning dating extreme value theory.

Area of Science:

• Cybersecurity
• Data Science
• Risk Management

Background:

• Modeling multivariate cyber risks is challenging due to high dimensionality and heavy-tailed risk patterns.
• Existing statistical methods struggle with the complexity of cyber risk data.

Purpose of the Study:

• To develop a novel approach for modeling multivariate cyber risks.
• To leverage deep learning and extreme value theory for enhanced risk prediction.
• To address the limitations of traditional statistical modeling in cybersecurity.

Main Methods:

• Integration of deep learning techniques for accurate point predictions.
• Application of extreme value theory for reliable high quantile predictions.
• Validation through simulation studies and empirical data analysis.

Main Results:

• The proposed model demonstrates high accuracy in point predictions.
• The model effectively provides satisfactory predictions for extreme cyber risk events.
• Both simulation and empirical studies confirm the model's robust performance.

Conclusions:

• The novel approach successfully models multivariate cyber risks.
• The combined deep learning and extreme value theory method offers superior prediction capabilities.
• This framework enhances the ability to manage and predict complex cybersecurity threats.