Risk-Based Vulnerability Management Using Bayesian Attack-Path Scoring
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Solution Overview
Problem
Existing methods for generating attack graphs face challenges such as difficulty in capturing input information, lack of scalability, and lack of usability by non-technical personnel, making it hard to prioritize remediation efforts effectively.
Innovation Solution
An improved approach using natural language processing (NLP) to identify pre- and post-conditions, combined with graph database technology to store attack graphs, and Bayesian networks for risk evaluation, enabling the generation and analysis of attack paths across enterprise networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional attack graph generation methods are used, then comprehensive vulnerability analysis is achieved, but scalability and usability deteriorate
Solution Approach 1:
The patent introduces Bayesian Networks as an intermediary layer between the attack graph data structure and the user interface. This intermediary computes risk scores automatically using probability theory, mediating the complex vulnerability data and presenting simplified risk assessments to users, thereby maintaining analysis accuracy while reducing operational complexity
Solution Approach 2:
The patent transforms the attack graph from a static structural representation to a dynamic risk assessment model by introducing probability parameters. Vulnerabilities are evaluated not just by their existence but by computed risk scores that incorporate likelihood and impact parameters, enabling scalable prioritization without sacrificing analysis depth
2Measurement precision
If detailed attack graph data is collected, then risk assessment accuracy is improved, but information capture difficulty increases
Solution Approach 1:
The patent creates a unified Bayesian Network model that serves multiple functions simultaneously: it collects vulnerability data, computes risk scores, prioritizes remediation efforts, and generates reports. This multi-functional approach consolidates data collection and analysis into a single framework, improving precision while reducing the difficulty of managing diverse data requirements
3Manufacturing precision
If comprehensive vulnerability data is analyzed, then remediation prioritization accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary risk score computations by pre-calculating probability distributions for vulnerabilities based on their inherent characteristics and contextual factors. This preliminary action prepares prioritization data in advance, enabling rapid remediation decisions without sacrificing accuracy, as the heavy computational lifting occurs before actual prioritization is needed
Data Source
AI summary
Various systems and methods for providing risk-based vulnerability management are described herein. A system is configured to access an attack graph, the attack graph including exploits represented as preconditions and postconditions; access vulnerability information of a plurality of nodes in the network, the vulnerability information including conditions of the plurality of nodes in the network; access a network connectivity graph that represents a logical network topology of the plurality of nodes in the network; identify a set of attack paths in the attack graph by comparing the conditions of the plurality of nodes in the network with preconditions and postconditions in the attack graph; calculate a risk score for each of the set of attack paths in the attack graph; and present the risk score for each of the set of attack paths.