Attack Graph Abstraction for Scalable Enterprise Risk Mitigation
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Solution Overview
Problem
Existing analytical attack graphs (AAGs) for enterprise networks are resource-intensive and hinder scalability due to their large size and frequent processing requirements, making it challenging to efficiently identify and mitigate cyber threats.
Innovation Solution
Implementing bisimulation techniques to abstract AAGs into smaller, abstract AAGs that preserve the structure and paths of the original AAGs, allowing for faster detection of defense cores and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If analytical attack graphs are generated and processed for large enterprise networks, then comprehensive security analysis and vulnerability identification are achieved, but resource consumption (memory and processing power) increases significantly
Solution Approach 1:
The patent segments the large enterprise network into multiple sub-networks or zones, generating separate analytical attack graphs for each segment rather than one monolithic graph. This division reduces the memory and processing requirements for each individual graph while maintaining comprehensive security coverage across the entire network through aggregated analysis of all segments.
Solution Approach 2:
The patent extracts and focuses analysis on critical attack paths and high-value targets within the network, removing less relevant nodes and edges from the analytical attack graph. This extraction approach maintains the ability to identify serious vulnerabilities while reducing graph size and computational resource consumption by eliminating redundant elements.
2Reliability
If analytical attack graphs are frequently generated and processed to keep up with network dynamics, then current security threats are detected, but processing time and resource intensity increase
Solution Approach 1:
The patent performs preliminary analysis by pre-identifying critical attack paths, high-value targets, and vulnerable components during graph generation. This preliminary action allows subsequent security analyses to focus only on these pre-identified elements, significantly reducing processing time for frequent threat detection while maintaining timely detection capability.
Solution Approach 2:
The patent applies partial action by performing security analysis on only the most critical portions of the network (key attack paths and high-value assets) rather than exhaustively analyzing every node and edge. This approach achieves timely threat detection for the most important vulnerabilities without the excessive processing time required for complete network analysis.
3Measurement precision
If complete and detailed AAGs are processed, then accurate security assessment is achieved, but scalability is hindered due to resource intensity
Solution Approach 1:
The patent applies local quality by varying the level of detail in different parts of the network analysis. Critical areas (high-value assets, vulnerable segments) are analyzed with high detail and precision, while less critical areas use simplified models. This approach maintains accurate security assessment for important regions while improving overall system scalability through selective detail.
Solution Approach 2:
The patent uses partial action by focusing computational resources on achieving accurate security assessment for the most critical network components rather than uniformly high precision across the entire network. This allows the system to scale by concentrating detailed analysis where it matters most while using simplified analysis elsewhere.
Data Source
AI summary
Implementations include methods, systems, computer-readable storage medium for mitigating cyber security risk of an enterprise network. A method includes: receiving an initial analytic attack graph (AAG) that is representative of paths within the enterprise network with respect to at least one target asset, the initial AAG comprising nodes and edges between the nodes; identifying, from the nodes of the initial AAG, a plurality of node groups, each node group including two or more nodes having at least one common attribute; generating an abstract AAG from the initial AAG, the abstract AAG including at least one abstract node, wherein each node group of the initial AAG is represented by a respective abstract node of the abstract AAG; determining a set of remedial actions at least partially based on the abstract AAG; and executing remedial actions in the set of remedial actions to reduce a cyber security risk to the enterprise network.