Automated Network Risk Detection via Attack Graph Simulation
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Solution Overview
Problem
Current security assessment methods for computer networks are manual, labor-intensive, and lack automation, making them inefficient and inaccurate, as they fail to consider business impacts and prioritize vulnerabilities effectively, leading to difficulties in identifying and mitigating risks in dynamic IT environments.
Innovation Solution
An automated system and method for performing security assessments that gather network information, create models, simulate attacks using attack graphs, calculate attack probabilities, and rank vulnerabilities based on risk, allowing for real-time prioritization and remediation of high-priority vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual security assessment methods are used, then security staff can perform assessments, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The system enables automated security assessments that perform themselves without continuous human intervention. The automated assessment engine continuously scans networks, identifies vulnerabilities, and generates reports autonomously, transforming security assessment from a manual service into a self-executing system that dramatically improves productivity while reducing labor intensity.
Solution Approach 2:
The patent replaces manual mechanical assessment processes with automated computational systems. The automated assessment engine uses software-based vulnerability scanning and analysis instead of human analysts manually examining systems, substituting mechanical human labor with automated computational mechanisms to enhance efficiency and reduce operational burden.
2Measurement precision
If traditional vulnerability scanning is performed, then component-level vulnerabilities are detected, but business impact and overall security context are not understood
Solution Approach 1:
The system merges component-level vulnerability detection with business context analysis into a unified assessment framework. The automated assessment engine combines technical vulnerability data with business impact information, asset criticality, and security context to provide comprehensive vulnerability evaluation that maintains both detection precision and contextual understanding.
Solution Approach 2:
The automated assessment engine performs multiple functions simultaneously: it detects technical vulnerabilities, analyzes business impact, evaluates security context, and prioritizes risks. This multi-functional system eliminates the need for separate component-level scanning and business impact analysis, preserving both detection accuracy and contextual information in a single integrated process.
3Reliability
If security assessments are performed manually several times per year, then periodic security checks are conducted, but continuous monitoring and real-time accuracy are compromised
Solution Approach 1:
The system implements continuous security assessment through the automated assessment engine that operates continuously rather than periodically. The engine performs ongoing vulnerability scanning and analysis, eliminating gaps between assessments and providing real-time security posture information. This continuous operation maintains high reliability by ensuring vulnerabilities are detected promptly while reducing the time loss associated with periodic manual assessments.
4Measurement precision
If comprehensive vulnerability analysis is attempted, then all vulnerabilities are identified, but the complexity and time required for assessment increases dramatically
Solution Approach 1:
The system segments the comprehensive vulnerability analysis into structured phases: data collection, vulnerability scanning, business impact analysis, and risk prioritization. The automated assessment engine processes vulnerabilities in organized stages, managing complexity through systematic segmentation while maintaining complete identification of all vulnerabilities through thorough multi-phase analysis.
Solution Approach 2:
The system changes assessment parameters dynamically based on network size, vulnerability severity, and business context. The automated engine adjusts scanning depth, analysis intensity, and reporting detail to optimize the balance between comprehensive vulnerability identification and manageable system complexity, maintaining completeness while controlling operational complexity through adaptive parameter adjustment.
Data Source
AI summary
The present invention provides systems and methods for risk detection and analysis in a computer network. Computerized, automated systems and methods can be provided. Raw vulnerability information and network information can be utilized in determining actual vulnerability information associated with network nodes. Methods are provided in which computer networks are modeled, and the models utilized in performing attack simulations and determining risks associated with vulnerabilities. Risks can be evaluated and prioritized, and fix information can be provided.