Network Node Classification for Multi-Method Penetration Testing
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Solution Overview
Problem
Current penetration testing methods are inefficient in identifying multiple attack methods for networked systems, often missing additional vulnerabilities beyond the first detected method, leading to incomplete security measures and prolonged discovery periods for new threats.
Innovation Solution
A method that assigns network nodes to disjoint classes based on compromisability, allowing for delayed conversion to a 'red' state after initial compromisability is determined, enabling continued testing for additional attack methods and providing remediation recommendations for multiple vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If penetration testing systems immediately mark a network node as compromised after detecting the first attack method, then the testing process is simplified and faster, but additional vulnerabilities and attack methods are missed
Solution Approach 1:
The system performs preliminary classification of network nodes into disjoint sets based on initial compromisability assessment before conducting full penetration testing. This preliminary action organizes the testing process to systematically explore multiple attack methods without missing vulnerabilities, resolving the contradiction between testing speed and detection completeness.
Solution Approach 2:
The patent segments network nodes into disjoint classification sets based on their compromisability status and testing progress. By dividing the network space into distinct categories (e.g., untested, partially tested, fully tested nodes), the system can efficiently manage testing resources while ensuring comprehensive vulnerability detection across all nodes.
2Reliability
If external consultants are hired for penetration testing, then expertise and quality improve, but cost increases and testing frequency decreases
Solution Approach 1:
The penetration testing system is designed to be largely autonomous, automatically classifying network nodes, selecting attack methods, and conducting tests without requiring continuous human intervention. This self-service capability allows organizations to perform frequent penetration testing in-house, maintaining high quality through automated consistency while achieving high testing frequency, thus resolving the contradiction between reliability and productivity.
Solution Approach 2:
The system dynamically adjusts testing parameters such as the depth of analysis, types of attacks attempted, and resource allocation based on the classified status of network nodes. This adaptive parameter adjustment ensures high-quality testing focused on critical areas while maintaining efficient resource usage, enabling frequent testing without compromising quality.
3Measurement precision
If full penetration testing is conducted against all threats, then vulnerability detection completeness improves, but testing time increases significantly
Solution Approach 1:
The system performs preliminary classification of network nodes into disjoint sets based on initial reconnaissance and risk assessment before conducting full penetration testing. This preliminary action identifies high-value targets and prioritizes them for detailed testing, ensuring comprehensive vulnerability detection in critical areas while reducing time spent on lower-priority nodes, thus resolving the contradiction between detection completeness and testing duration.
Solution Approach 2:
The patent applies different levels of testing intensity to different network nodes based on their classification and risk profile. High-value or critical nodes receive exhaustive testing with multiple attack methods, while lower-priority nodes receive streamlined testing. This local quality approach ensures thorough vulnerability detection where needed while minimizing overall testing time.
Data Source
AI summary
Methods and systems for penetration testing of a networked system involve assigning network nodes to disjoint classes based on current information about the compromisability of the network nodes. The classes distinguish between nodes not currently known to be compromisable, nodes that only recently have become known to be compromisable, e.g., by a first method of a attack, and nodes that have been known for a longer time to be compromisable. Nodes that only recently have become known to be compromisable can be re-targeted by the penetration testing system to determine whether such nodes can be compromised using multiple methods of attack and not just using the first method of attack.