Internet Health Forecasting With Cyber Enrichment Simulation
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Solution Overview
Problem
Current cybersecurity solutions lack comprehensive contextual data for non-physical risks, leading to inadequate risk management and model errors, especially in the evolving landscape of cybersecurity and business continuity, with insufficient integration of contextual values and hypothetical histories.
Innovation Solution
A system and method for large-scale internet health forecasting and noise analysis using cyber enrichment service databases, statistical models, and generative ML models to provide comprehensive internet infrastructure monitoring and proactive threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive contextual data collection and simulation systems are implemented, then cybersecurity risk management capability is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the internet infrastructure into discrete entities (IP addresses, autonomous systems, networks) that can be independently monitored and simulated. This segmentation allows comprehensive coverage while managing complexity through modular data structures and targeted simulations rather than holistic system analysis.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and storing contextual data about internet entities, relationships, and configurations before threats occur. This pre-positioning of data enables rapid risk assessment and simulation when security events occur, improving response capability without requiring complex real-time processing during critical moments.
2Measurement precision
If comprehensive internet infrastructure monitoring is implemented, then threat detection capability is improved, but data processing load and computational resources increase
Solution Approach 1:
The system applies local quality by focusing monitoring and simulation efforts on specific internet entities and their relationships rather than uniformly analyzing the entire internet infrastructure. This allows precise threat detection for targeted entities while reducing overall computational load by not processing all data at equal depth.
Solution Approach 2:
The system creates simulated copies of internet entities and their relationships to model threat scenarios without requiring direct manipulation of actual infrastructure data. These simulations enable comprehensive threat analysis while preserving computational resources by working with virtual representations rather than real-time production data.
3Measurement precision
If contextual values and hypothetical histories are integrated into risk models, then risk assessment accuracy is improved, but model complexity and data requirements increase
Solution Approach 1:
The system adds the dimension of time by integrating hypothetical histories and temporal relationships into risk models. This allows assessment of how entities and their relationships have evolved over time, improving risk accuracy by considering historical patterns while managing complexity through structured temporal data representation rather than unbounded historical analysis.
Data Source
AI summary
A system and method for large-scale internet health forecasting and internet noise analysis. The system and method feature the ability to scan for, ingest and process, and then use various data stores for capturing entity data, their relationships, and actions associated with them. This data forms the basis for cyber enrichment service databases which can used to provide information responsive to user submitted queries as well as to produce large-scale (e.g., Internet scale) simulation models using statistical models, generative ML models, massively multiplayer online gaming simulation systems, or full discrete event simulation engines or some combination. User submitted queries can be ran against the raw data or against simulations to provide simulation results that can be used improve cybersecurity for an organization against actual observed behaviors or simulations.