Network Reliability Prediction via Attack Graph Statistical Models
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Solution Overview
Problem
Current cybersecurity measures lack effective methods to quantify and manage the reliability of computer network systems based on their vulnerabilities, making it difficult to predict and prevent hacking events.
Innovation Solution
The development of statistical models using the Common Vulnerability Scoring System (CVSS) and Markov processes to estimate the expected path length and minimum number of steps for an attacker to compromise a network, along with parametric and non-parametric reliability functions, to enhance cybersecurity specialists' ability to monitor and improve network security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical models and Markov processes are used to estimate attack paths, then the ability to predict and prevent hacking events is improved, but the complexity of the cybersecurity system increases
Solution Approach 1:
The patent introduces statistical models and Markov processes as intermediary computational frameworks that bridge vulnerability data and security predictions. These models act as mediators that process complex vulnerability interactions and transform them into actionable reliability metrics, allowing the system to handle complexity through structured mathematical abstractions rather than direct system complexity
Solution Approach 2:
The patent transforms security assessment from qualitative vulnerability descriptions to quantitative reliability parameters. By converting vulnerability data into statistical parameters and probability distributions, the system enables mathematical analysis and prediction of attack paths, transforming the nature of security measurement from subjective to objective and measurable
2Measurement precision
If the expected path length and minimum steps for attackers are calculated, then the precision of security risk assessment is improved, but the computational time and resources increase
Solution Approach 1:
The patent pre-calculates and stores statistical parameters and probability distributions for common attack patterns and vulnerability combinations. By preparing these computational results in advance, the system reduces real-time computational requirements when assessing specific security risks, allowing rapid querying of pre-computed attack path statistics
Solution Approach 2:
The patent uses simplified statistical representations and probability distributions that replicate complex attack dynamics without requiring full simulation of every possible attack scenario. These statistical models serve as computationally efficient copies that capture essential security behavior patterns without the overhead of exhaustive analysis
Data Source
AI summary
Statistical models for predicting minimum number of step data of an attacker and reliability functions of a computer network system are described. The models are based on utilizing vulnerability information along with an attack graph. Using the model, it is possible to identify the interaction among vulnerabilities and individual variables or risk factors that drive the minimum number of step data. Gaining a better understanding of the relationship between the vulnerabilities and their interactions can provide security administrators with a better view and understanding of their security status.