Network Traffic Inference for Third-Party Security Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cybersecurity assessment techniques often fail to provide a comprehensive or accurate evaluation of an organization's security due to complex business relationships with unaudited and potentially insecure third-party entities, which can compromise the security of networked systems.
Innovation Solution
The system infers relationships among entities by analyzing network traffic, using techniques such as observing device activity, IP address mapping, and protocol analysis to identify business partnerships and relationship types without direct engagement, applying confidence values based on frequency and contextual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cybersecurity assessment focuses only on audited systems, then assessment scope is limited and manageable, but security evaluation becomes incomplete and inaccurate due to unaudited third-party systems
Solution Approach 1:
The patent introduces network traffic data as an intermediary medium that indirectly reveals relationships between audited and unaudited systems. By analyzing traffic patterns, device identifiers, and communication metadata, the system infers third-party relationships without directly accessing or auditing those external systems, thus expanding evaluation scope while managing complexity through indirect observation
Solution Approach 2:
The system performs preliminary identification of third-party relationships by analyzing historical network traffic data before conducting security assessments. This preliminary action maps organizational boundaries and relationships in advance, allowing the assessment to include inferred third-party risks without adding complexity during the actual assessment phase
2Loss of information
If network traffic monitoring is implemented to identify third-party relationships, then business intelligence is improved, but data processing complexity and computational resources increase
Solution Approach 1:
The patent extracts only the essential elements needed for relationship identification from network traffic data, such as device identifiers, IP addresses, communication patterns, and timing information. By taking out only these critical features rather than processing complete raw traffic data, the system recovers business relationship information while minimizing data processing complexity and computational overhead
Solution Approach 2:
The system applies partial monitoring by focusing on specific traffic characteristics and communication patterns that are most indicative of business relationships, rather than analyzing all network traffic in detail. This selective approach recovers sufficient business intelligence while reducing the computational burden of processing complete network datasets
3Measurement precision
If comprehensive network traffic analysis is performed across all entities, then relationship detection accuracy is improved, but time and computational resources required increase
Solution Approach 1:
The system performs preliminary filtering and aggregation of network traffic data to identify potential relationships before conducting detailed analysis. By pre-processing data to highlight significant communication patterns and group related traffic, the system achieves accurate relationship detection while reducing the time required for comprehensive analysis of all raw traffic data
Solution Approach 2:
The patent applies targeted analysis by focusing computational resources on specific traffic patterns and communication channels that are most likely to reveal business relationships. Rather than uniformly analyzing all traffic with equal depth, the system applies partial excessive action by intensively analyzing only the most promising data segments, achieving high detection accuracy with reduced overall analysis time
Data Source
AI summary
A number of techniques facilitate generation of data points from observations about network traffic. An inferencing system can use these data points to determine whether a relationship exists between two entities or whether an existing relationship has terminated, without any external knowledge of the existence of or termination of such a relationship.