Intelligent Search Network for Time-Based Compromised Node Isolation
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Solution Overview
Problem
Existing network vulnerability detection systems struggle with delayed incident reporting and temporal gaps, which are exploited by malicious actors to hide compromised nodes, making it difficult to detect and isolate such nodes effectively.
Innovation Solution
The system utilizes event rate gradients and temporal sequences to identify compromised nodes by analyzing event histories, applying acceleration thresholds, and expanding search windows to account for malicious delays, followed by restricting network access or applying monitoring filters to isolate compromised devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network vulnerability detection systems use traditional incident reporting methods, then the system structure remains simple, but detection accuracy deteriorates due to temporal gaps and delayed incident reporting
Solution Approach 1:
The system dynamically adjusts the search window time period based on event rate gradients. When a node shows suspicious activity patterns, the search window is expanded to capture potential compromised nodes that may have been infected earlier. This dynamic adaptation allows the system to maintain high detection accuracy while managing complexity through context-aware parameter adjustment rather than fixed complex structures.
Solution Approach 2:
The system changes the time parameter of the search window based on detected event rate gradients. By adjusting the time window parameter dynamically according to the gradient magnitude and direction, the system optimizes its detection capability for different attack patterns without requiring a completely complex system architecture, thus resolving the contradiction between accuracy and complexity.
2Reliability
If the search window time period is extended to capture more historical data, then detection capability improves, but processing time and computational load increase
Solution Approach 1:
The system pre-calculates and stores event rate gradients for network nodes during normal operation. This preliminary computation allows the system to quickly identify suspicious patterns without performing heavy computational tasks during actual detection events. By preparing gradient data in advance, the system reduces processing time while maintaining the ability to extend the search window when needed.
Solution Approach 2:
The system dynamically adjusts the search window based on real-time event rate gradient analysis. When gradients indicate normal activity, the system uses a standard search window to minimize processing time. When gradients show suspicious patterns, the system expands the search window to improve detection capability. This dynamic approach allows the system to optimize between processing time and detection capability based on actual network conditions.
3Reliability
If the system applies strict isolation to detected compromised nodes, then security improves, but network availability deteriorates due to false positives
Solution Approach 1:
The system performs preliminary analysis by calculating event rate gradients and identifying suspicious patterns before applying isolation. This preliminary detection phase allows the system to distinguish between truly compromised nodes and nodes with temporary or false anomalies. By preparing and analyzing gradient data in advance, the system can make more informed decisions about isolation, reducing false positives while maintaining security.
Solution Approach 2:
The system continuously monitors event rate gradients and provides feedback on the effectiveness of isolation decisions. By analyzing changes in event patterns after potential isolation actions, the system can adjust its approach to balance security and availability. The feedback mechanism allows the system to learn from past decisions and refine future isolation actions, reducing the impact of false positives on network availability while maintaining strong security posture.
Data Source
AI summary
A method and related systems for isolating compromised edge nodes in a computing device network based on tracking event patterns in off-network data by obtaining an anomaly indication for an event type associated with a set of event participant identifiers indicated by temporal sequences for a network. Some embodiments may determine a set of compromised nodes based on the events of the event type and restrict or apply a monitoring filter to traffic for the set of compromised nodes.