Network Intrusion Detection via Transaction Flow Pattern Analysis
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Solution Overview
Problem
Current network intrusion detection systems face challenges in effectively detecting security breaches, particularly insider threats, due to overwhelming amounts of data and the difficulty in capturing relevant insights from granular data levels, which can diminish their effectiveness.
Innovation Solution
The system analyzes patterns of transaction flows through software applications by establishing a baseline of normal flow patterns and comparing them to identify deviations, using application mapping data to generate flow patterns and detect potential security intrusions, with a focus on presenting pertinent information to users without overwhelming them with detail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sophisticated pattern recognition algorithms are used to detect security breaches, then detection capability is improved, but data volume overwhelms the algorithms to the point of diminishing utility
Solution Approach 1:
The patent extracts and analyzes only the most critical flow attributes (source IP, destination IP, protocol, port numbers, packet counts) rather than processing all raw network data. This selective extraction reduces data volume to manageable levels while maintaining sufficient information for effective intrusion detection through pattern recognition algorithms.
Solution Approach 2:
The patent segments network traffic data into discrete flow records grouped by five-tuple characteristics (source IP, destination IP, source port, destination port, protocol). This segmentation organizes massive data volumes into manageable units that can be processed efficiently by pattern recognition algorithms, reducing complexity while preserving detection capability.
2Measurement precision
If granular-level data is analyzed to detect insider threats, then detection precision is improved, but system complexity increases and effectiveness diminishes
Solution Approach 1:
The patent applies different analysis depths to different data types: flow-level aggregation for most traffic, but granular packet-level analysis only when anomalies are detected. This local quality approach maintains high detection precision for insider threats while avoiding unnecessary complexity in processing normal traffic, effectively balancing precision and system complexity.
Solution Approach 2:
The patent performs preliminary flow aggregation and pattern establishment before conducting detailed analysis. By first establishing baseline flow patterns at the flow level and only drilling down to granular details when deviations are detected, the system maintains detection precision for insider threats while preventing system complexity from overwhelming the analysis process.
3Adaptability or versatility
If all available data is collected for intrusion detection, then comprehensive coverage is improved, but data processing effectiveness decreases due to overwhelming volume
Solution Approach 1:
The patent introduces flow records as an intermediary data structure between raw network packets and security analysis algorithms. This intermediary layer aggregates and pre-processes data, maintaining comprehensive coverage of network traffic while reducing the volume and complexity of data that reaches the analysis stage, thereby improving processing effectiveness.
Solution Approach 2:
The patent transforms detailed packet-level data into a lower-dimensional flow record representation by aggregating packets sharing common characteristics. This dimensional reduction maintains comprehensive coverage through flow-level analysis while significantly improving processing effectiveness by reducing data complexity and volume for the security algorithms.
Data Source
AI summary
Disclosed herein are techniques for detecting possible security intrusions in a computer network. The security intrusion detection may be based on analyzing patterns of how transactions flow through one or more software applications. For example, patterns of transaction flows are determined for an initial time period to establish a baseline of normal flow patterns. These normal flow patterns may be compared with patterns for transaction flows for a later time period. Deviations in the patterns of transaction flow may indicate a possible security intrusion.