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Modeling Multivariate Cyber Risks: Deep Learning Dating Extreme Value Theory
- Source :
- J Appl Stat
- Publication Year :
- 2021
-
Abstract
- Modeling cyber risks has been an important but challenging task in the domain of cyber security. It is mainly because of the high dimensionality and heavy tails of risk patterns. Those obstacles have hindered the development of statistical modeling of the multivariate cyber risks. In this work, we propose a novel approach for modeling the multivariate cyber risks which relies on the deep learning and extreme value theory. The proposed model not only enjoys the high accurate point predictions via deep learning but also can provide the satisfactory high quantile prediction via extreme value theory. The simulation study shows that the proposed model can model the multivariate cyber risks very well and provide satisfactory prediction performances. The empirical evidence based on real honeypot attack data also shows that the proposed model has very satisfactory prediction performances.<br />25 pages
- Subjects :
- FOS: Computer and information sciences
Statistics and Probability
Multivariate statistics
Computer science
0211 other engineering and technologies
Machine Learning (stat.ML)
02 engineering and technology
Machine learning
computer.software_genre
01 natural sciences
Statistics - Applications
Domain (software engineering)
Task (project management)
010104 statistics & probability
Statistics - Machine Learning
Applications (stat.AP)
0101 mathematics
Extreme value theory
ComputingMilieux_MISCELLANEOUS
021103 operations research
business.industry
Deep learning
Articles
Heavy-tailed distribution
Artificial intelligence
Statistics, Probability and Uncertainty
High dimensionality
business
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- J Appl Stat
- Accession number :
- edsair.doi.dedup.....86c52ef4461c372fb37fe6d5734f6cb1