Forecasting Network Security Incidents Using CGLV Model
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Solution Overview
Problem
Accurately predicting future security incidents in modern network environments is challenging due to the high number of network devices and potential threats, making it difficult for organizations to plan and mitigate against such incidents, which can result in costly damage.
Innovation Solution
A computer-implemented method using a Coupled Gaussian Latent Variable (CGLV) model to forecast future security incidents by analyzing past inside-in and outside-in security features, employing dynamic time warping to generate similarity scores, and performing security actions on network devices based on the forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security monitoring methods are used to track every potential security incident, then comprehensive security coverage is achieved, but the system complexity and computational resources required become unmanageable due to the high number of network devices and threats
Solution Approach 1:
The patent introduces a CGLV model as an intermediary between raw security data and security predictions. This model includes a latent variable that captures the underlying security state, mediating between observed security features and future security incidents. This intermediary structure simplifies the monitoring system by focusing on key latent factors rather than tracking every individual threat across all devices.
Solution Approach 2:
The patent transforms the security monitoring approach by changing parameters from tracking individual security incidents to modeling temporal dynamics of security features. The CGLV model uses time-series analysis of security features (such as vulnerability counts, patch levels, threat landscape metrics) to predict future security states, shifting from event-based monitoring to parameter-based forecasting.
2Loss of information
If comprehensive monitoring of all network devices is performed, then complete security visibility is achieved, but the time and computational resources required become prohibitively large
Solution Approach 1:
The patent extracts the essential security information by identifying and modeling key security features that drive future security incidents. Rather than analyzing all security data from all devices, the CGLV model extracts temporal patterns from representative security features (vulnerability metrics, patching rates, threat intelligence) to infer the security state of the entire network, significantly reducing information processing requirements.
3Measurement precision
If accurate prediction of future security incidents is achieved, then effective security planning and mitigation become possible, but the complexity of the prediction model increases significantly
Solution Approach 1:
The patent segments the security prediction problem into distinct components: observed security features, latent security state variables, and future security incident predictions. The CGLV model separates the temporal dynamics of different security features and their relationships through the latent variable structure, allowing accurate predictions while maintaining model interpretability and manageable complexity through modular computation.
Data Source
AI summary
Securing network devices by forecasting future security incidents for a network based on past security incidents. In one embodiment, a method may include constructing past inside-in security features for a network, constructing past outside-in security features for the network, and employing dynamic time warping to generate a similarity score for each security feature pair in the past inside-in security features, in the past outside-in security features, and between the past inside-in security features and the past outside-in security features. The method may further include generating a Coupled Gaussian Latent Variable (CGLV) model based on the similarity scores, forecasting future inside-in security features for the network using the CGLV model, and performing a security action on one or more network devices of the network based on the forecasted future inside-in security features for the network.