Cross-Platform Intrusion Detection Aggregation
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Solution Overview
Problem
In large organizations with multiple geographic locations, individual intrusion detection systems operate in silos, lacking coordination and control to effectively identify and quarantine potential unauthorized intrusions, leading to missed indicators across different teams.
Innovation Solution
A cross-platform user event aggregation system that uses a centralized data repository and machine learning algorithms to process exposure events from multiple detection systems, determining patterns and re-training algorithms to identify and assess potential intrusions, enabling efficient communication and coordination between detection systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual intrusion detection systems operate independently in each department, then each system can be simple and focused on its specific department, but the overall detection capability is reduced due to lack of coordination and missed indicators across teams
Solution Approach 1:
The patent combines multiple independent intrusion detection systems into a unified cross-platform aggregation system that collects, normalizes, and analyzes exposure events from multiple departments and detection systems centrally, enabling coordinated detection while maintaining individual system independence
Solution Approach 2:
The aggregation system serves multiple functions: collecting events from various detection systems, normalizing different event formats, analyzing patterns across platforms, and providing centralized reporting, making it a universal solution for multi-department intrusion detection
2Loss of information
If a centralized system aggregates data from multiple detection systems, then coordination and pattern identification improve, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces an aggregation system as an intermediary layer between individual detection systems and final analysis, which normalizes and standardizes exposure events from multiple sources before analysis, reducing complexity at individual system levels while enabling comprehensive coordination
Solution Approach 2:
The system segments the intrusion detection process into distinct modules: event collection from multiple sources, event normalization, pattern analysis, and reporting, allowing each component to be optimized independently while working together in a coordinated fashion
3Measurement precision
If machine learning algorithms are used to score exposure events, then detection accuracy improves, but the computational resources and processing time required increase
Solution Approach 1:
The patent applies machine learning algorithms selectively to normalize and score exposure events based on their relevance and risk level, rather than processing all events with equal computational intensity, optimizing resource usage while maintaining detection accuracy for critical events
Data Source
AI summary
Systems, computer program products, and methods are described herein for cross platform user event record aggregation system. The present invention is configured to receive one or more exposure events from one or more detection systems; determine that a combination of at least a portion of the one or more exposure events indicates an intrusion in at least one of the one or more detection systems, thereby requiring elevated review of each exposure event; initiate the elevated review based on at least the indication of the intrusion; determine whether the intrusion is benign or harmful; and re-train the machine learning algorithm based on at least determining whether the intrusion is benign or harmful, thereby adjusting the score for future incidents of each exposure event in the combination of at least a portion of the one or more exposure events.