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Published on: November 21, 2023
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Statistical modeling of computer malware propagation dynamics in cyberspace.
Zijian Fang1, Peng Zhao2, Maochao Xu3
1Department of Statistics and Finance, University of Science and Technology of China, Hefei, Peoples Republic of China.
Journal of Applied Statistics
|June 16, 2022
Summary
This study introduces a Bayesian time series model to accurately predict computer malware propagation. The model effectively forecasts cyber attack dynamics without needing network or attack details.
Area of Science:
- Cybersecurity and computational modeling.
- Statistical analysis of dynamical systems.
Background:
- Understanding computer malicious software (malware) propagation is crucial for cybersecurity.
- Existing models may require detailed network topology or attack-defense mechanisms.
Purpose of the Study:
- To develop a statistical model for macro-level cyber attack evolution.
- To accurately model and predict computer malware propagation dynamics.
- To provide a parsimonious model with predictive capabilities.
Main Methods:
- Bayesian structural time series approach.
- Statistical modeling of macro-level dynamics.
- Simulation studies for validation.
Main Results:
- The proposed model accurately fits and predicts malware propagation.
- The model accommodates uncertainty and provides predictive distributions.
- It does not require prior knowledge of network topology or attack-defense interactions.
Conclusions:
- The Bayesian structural time series model is effective for analyzing cyber threats.
- This approach offers accurate predictions for malware spread, including Conficker and Code Red.
- The model's parsimony and ability to handle uncertainty make it a valuable tool for cybersecurity research.
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