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Related Experiment Video

Updated: Aug 9, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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

Mingyue Zhang Wu¹, Jinzhu Luo², Xing Fang²

¹School of Mathematics and Statistics and RIMS, Jiangsu Provincial Key Laboratory of Educational Big Data Science and Engineering, Jiangsu Normal University, China.

Journal of Applied Statistics

|February 23, 2023

Summary

This summary is machine-generated.

This study introduces a new method for modeling complex cyber risks using deep learning and extreme value theory. The approach improves predictions for cybersecurity threats, enhancing overall risk management.

Keywords:

Cyber attacks GPD LSTM heavy tail high-dimensional dependence

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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.