Graph-Based Vulnerability Prediction for Remote Asset Profiling
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Solution Overview
Problem
Existing cybersecurity systems lack automated methods for profiling remote assets to identify and predict exploitable vulnerabilities based on known vulnerabilities, and they do not provide real-time or near-real-time infrastructure for vulnerability prediction.
Innovation Solution
A system that loads cybersecurity vulnerability ontologies into a graph database, profiles target assets, and uses query modules to predict vulnerabilities by linking asset traits to known vulnerabilities, employing a message-based SOA design or schedular-based design with API architecture for real-time or batch processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated vulnerability prediction systems are implemented, then vulnerability detection speed and timeliness are improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The system pre-loads vulnerability ontologies, asset trait profiles, and vulnerability databases into the graph database before actual vulnerability prediction is needed. This preliminary preparation of data structures and relationships enables rapid real-time prediction without complex processing during the actual detection phase, resolving the contradiction between fast detection and system complexity.
Solution Approach 2:
The patent introduces a graph database as an intermediary layer between raw vulnerability data and prediction results. The graph database stores pre-processed relationships between assets, traits, and vulnerabilities, acting as a mediator that simplifies complex queries and enables rapid prediction without requiring complex real-time processing infrastructure.
2Measurement precision
If comprehensive asset profiling is performed to identify all vulnerabilities, then vulnerability prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
Asset traits and vulnerability relationships are profiled and stored in the graph database in advance, before actual vulnerability prediction is needed. This preliminary profiling captures comprehensive asset characteristics and their relationships to vulnerabilities, enabling accurate real-time prediction without requiring time-consuming processing during the actual detection phase.
Solution Approach 2:
The system dynamically adapts the profiling depth based on the specific prediction context. The graph database structure allows the system to query only the relevant asset traits and vulnerability relationships needed for each specific prediction request, rather than always performing comprehensive profiling, thus balancing accuracy with processing efficiency.
3Speed
If real-time vulnerability prediction is implemented, then security response time is improved, but system resource consumption increases
Solution Approach 1:
All vulnerability data, asset profiles, and relationship mappings are pre-loaded into the graph database structure before real-time prediction is needed. This preliminary organization of data enables rapid real-time queries without requiring heavy computational resources during the actual prediction phase, resolving the contradiction between fast prediction and resource consumption.
Solution Approach 2:
The system creates a simplified copy of the vulnerability knowledge base in the graph database that contains only the essential relationships and data structures needed for rapid prediction. This copied structure enables fast real-time queries without requiring access to the full complexity of the original vulnerability databases, reducing resource consumption during real-time operation.
Data Source
AI summary
Techniques are disclosed for predicting cybersecurity vulnerabilities automatically in IT assets/targets based on known vulnerabilities of various available technologies/products. This is accomplished by loading and linking one or more ontologies in a graph database containing vulnerability information about the technologies. The assets/targets preferably belong to a bug-bounty program. An optional discovery tool maps the attack surface of each target. A profiler collects the various technologies or traits used by the target and links them to the target. Then the graph database is queried to predict the cybersecurity vulnerabilities associated with the traits and consequently with the targets. The system is preferably implemented with a service-oriented architecture (SOA) so feedback/predictions can be provided to the user in near/real-time.