Collaboration Value Vector Matching for Cyber Threat Mitigation
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Solution Overview
Problem
Organizations face challenges in selecting optimal collaboration partners for sharing security data to enhance cyber threat prediction and mitigation, as existing methods lack effective processes for determining beneficial partnerships, leading to limited prediction performance and scarce information sharing.
Innovation Solution
A system that receives collaboration value vectors from entities, sorts them, and determines matching partners using centralized and distributed assignment techniques to maximize the number of partners while ensuring privacy and trust, allowing entities to select partners based on expected benefits and target numbers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If companies share security data with multiple partners, then the accuracy of security intelligence and prediction performance is improved, but the complexity of determining optimal collaborating relationships increases
Solution Approach 1:
The patent introduces a central authority as an intermediary that receives collaboration value vectors from all companies, performs centralized matching computations, and returns optimized partner lists. This mediator handles the complex optimization problem centrally, allowing companies to benefit from accurate security intelligence sharing without individually managing the computational complexity of determining optimal relationships.
Solution Approach 2:
Each company independently computes its own collaboration value vectors based on its security data and preferences, then submits these to the central authority. The system allows companies to self-determine their collaboration preferences while the central authority handles the complex matching optimization, combining self-service with centralized coordination.
2Ease of operation
If companies autonomously select collaboration partners, then the ease of operation is improved, but the optimality of partner selection deteriorates
Solution Approach 1:
The system performs preliminary computations by having each company calculate its collaboration value vectors in advance, ranking potential partners based on expected benefits. This preliminary action prepares optimized partner lists that respect company preferences while maximizing security intelligence benefits, combining autonomous preference expression with optimized outcomes.
Solution Approach 2:
The central authority receives collaboration value vectors from companies, processes them through matching algorithms, and returns optimized partner lists as feedback. This feedback loop allows companies to see their preferences reflected in the results while benefiting from system-wide optimization, balancing autonomous selection with optimal outcomes.
Data Source
AI summary
One embodiment of the present invention provides a system for stable selection of collaborating partners for exchanging security data. During operation, the system receives vectors of collaboration values from a plurality of entities. A collaboration value is a measure of an expected benefit of collaborating with a respective entity. The system sorts each of the vectors by the collaboration values of the respective vector. The system then determines matching entities given a number of partners wanted by each organization in N. The system may add matching entities to lists of collaborating partners given the number of partners wanted by each organization in N. Subsequently, the system sends the lists of collaborating partners to facilitate exchanging security data with partners in the list of collaborating partners.