Distributed Data Reconstruction for Fragmented Exfiltration Detection
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Solution Overview
Problem
Existing systems struggle to detect and prevent the exfiltration of confidential information in distributed networks when data is fragmented and obfuscated through tactics like breaking it up into smaller pieces, using cryptography, and collaborating with multiple individuals, making it difficult to trace and analyze effectively.
Innovation Solution
A system that initiates network flow analysis, assigns indicators to specialized access nodes, executes cuts to isolate these nodes, captures discrete data segments, constructs arrangements, and uses a sensitive information detection engine to determine the presence of sensitive information, employing machine learning for pattern recognition and response actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is fragmented into smaller pieces and distributed across multiple nodes, then data protection against exfiltration is improved, but detection precision deteriorates
Solution Approach 1:
The system segments data into discrete data segments distributed across multiple specialized access nodes in the network. Each node holds only a portion of the complete information, preventing any single point of exfiltration while maintaining the ability to reconstruct and detect fragmented data patterns through network flow analysis
Solution Approach 2:
The system implements nested analysis by constructing multiple arrangements of data segments at different levels of organization. The detection engine examines nested patterns where combinations of segments form meaningful information, allowing detection of exfiltration attempts that span multiple fragmented pieces across the network
2Measurement precision
If network flow analysis captures and stores all discrete data segments, then detection capability is improved, but computing resources increase
Solution Approach 1:
The system extracts only the necessary discrete data segments from network traffic for analysis, storing them temporarily in storage devices associated with specialized access nodes. The detection engine extracts meaningful patterns from these segments without requiring analysis of entire network traffic volumes, reducing computational overhead while maintaining detection effectiveness
Solution Approach 2:
The system performs partial analysis by examining specific combinations of data segments rather than analyzing all possible arrangements. The detection engine focuses on partial patterns that indicate exfiltration attempts, avoiding the excessive computational burden of complete exhaustive analysis while maintaining sufficient detection capability
3Measurement precision
If multiple cuts are executed to isolate different specialized access nodes, then detection coverage is improved, but operation complexity increases
Solution Approach 1:
The system executes multiple cuts periodically to isolate different specialized access nodes in sequence. Each cut captures data segments from specific nodes, and the detection engine periodically reconstructs arrangements to detect exfiltration patterns. This periodic execution of cuts and analysis cycles provides comprehensive coverage while maintaining manageable operational complexity through systematic repetition
Data Source
AI summary
Systems, computer program products, and methods are described herein for data protection in a distributed network via reconstruction and analysis of data segments. The present invention is configured to initiate a network flow analysis to assign indicators to specialized access nodes of a network diagram, execute a first cut to remove predetermined nodes, store the corresponding discrete data segments for each of the first plurality of electronic communications at the nodes, construct a first arrangement of a selected group of the corresponding discrete data segments, determine a presence of sensitive information, and construct additional arrangements of the selected group of the corresponding discrete data segments to determine a presence of sensitive information.