July 2025
Next CVE Forecast
Forecasting Vulnerability Risk Through Time-Series Analysis
ONUR AKTAS - ABDULLAH ENES ONCU
Page Contents
System Overview and Methodology
The Next CVE Forecast system represents a groundbreaking approach to cybersecurity vulnerability management through predictive analytics. This innovative system leverages historical Common Vulnerabilities and Exposures (CVE) data from the National Vulnerability Database to forecast future vulnerability patterns across different technologies. By employing time series analysis techniques, particularly ARIMA (Autoregressive Integrated Moving Average) modeling, the system predicts both zero-day and known vulnerability frequency and severity for various technologies. The primary objective is to enable security teams to efficiently allocate their limited resources by correctly ranking technologies according to their vulnerability risk levels, achieving an impressive 87.8% pair agreement rate in risk ordering.
Technical Performance and Results
The comprehensive evaluation of multiple forecasting models revealed ARIMA(0,1,0) as the most effective approach, outperforming LSTM networks, transformers, and polynomial regression models in both accuracy and computational efficiency. The system demonstrates superior performance with 88.2% pairwise accuracy for technologies with over 20 CVEs, meaning that in nearly 9 out of 10 cases, the model correctly prioritizes the more vulnerable technology. The methodology includes the development of the S4E Risk Score algorithm, which combines vulnerability severity with temporal proximity using an exponential decay function, providing a more actionable metric for resource allocation than traditional CVSS scores alone.
Implementation and Future Applications
The system features a modular architecture designed for seamless integration with existing security workflows, including data collection modules, forecasting engines, and visualization interfaces. While the current approach is limited to technologies with sufficient historical data (approximately 40% coverage), it offers significant potential for enhancing vulnerability management practices. The methodology represents a shift toward more proactive and data-driven security operations, with future work focusing on enhanced data integration, version-aware modeling, and improved integration with popular security platforms to expand coverage and accuracy.
Key Performance Metrics
System Benefits
- Proactive Risk Management: Enables security teams to anticipate vulnerabilities before they emerge
- Resource Optimization: Provides data-driven prioritization for limited security resources
- Quantitative Assessment: Offers measurable risk scores for technology comparison
- Integration Ready: Designed for seamless integration with existing security workflows
- Continuous Learning: Adapts and improves as new vulnerability data becomes available
