Temporal Rate-Gradient Search for Compromised Network Nodes
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Solution Overview
Problem
Existing network vulnerability detection systems face challenges in identifying compromised nodes due to temporal gaps between incident occurrence and detection, which are often exploited by malicious actors to delay reporting, complicating the relationship between input data and output results.
Innovation Solution
The system employs rate gradients for event types based on temporal sequences to identify compromised nodes by analyzing event rates and rate gradients, using expanded time windows and geographic criteria to account for malicious delays, and applies monitoring filters or network access restrictions to isolate compromised devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network vulnerability detection systems are used, then basic security monitoring is maintained, but temporal gaps between incident occurrence and detection allow malicious actors to delay reporting and evade detection
Solution Approach 1:
The system performs preliminary actions by establishing baseline event rate gradients and temporal patterns before compromises occur. By continuously monitoring and comparing current event rates against historical gradients, the system can detect anomalies as they deviate from established patterns, reducing the temporal gap between incident occurrence and detection while maintaining high reliability through pattern-based detection rather than reactive response
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing current event rates against historical gradients and adjusting detection thresholds dynamically. This feedback loop allows the system to adapt to changing network conditions and maintain accurate detection timing, closing the temporal gap through real-time rate gradient analysis that provides immediate feedback on anomalous behavior
2Ease of operation
If detection systems use fixed time windows for analysis, then processing is simplified, but malicious actors can exploit these fixed windows by delaying incident reporting
Solution Approach 1:
The system transitions from fixed time windows to dynamic time-based analysis by continuously calculating event rate gradients across varying temporal intervals. This dynamic approach allows the detection mechanism to adapt its analysis window based on detected patterns and anomalies, maintaining simplicity through automated gradient computation while significantly improving reliability against delayed reporting strategies
Solution Approach 2:
The system changes the parameter of time window flexibility by using variable time intervals for rate gradient calculation rather than fixed windows. This parameter change enables the system to handle delayed reporting by adjusting the temporal scope of analysis dynamically, maintaining ease of operation through automated parameter adjustment while improving detection accuracy
3Reliability
If the system analyzes all temporal sequences for compromised nodes, then detection coverage is maximized, but processing complexity and computational resources increase
Solution Approach 1:
The system extracts and focuses analysis on the most critical aspect of temporal sequences - the event rate gradients - rather than processing every individual event in detail. By extracting the rate gradient information, the system maintains comprehensive detection coverage while significantly reducing processing complexity through this selective feature extraction approach
Solution Approach 2:
The system applies local quality by focusing computational resources on specific temporal patterns and rate gradients that indicate compromise, rather than uniformly processing all temporal sequences. This allows the system to maintain high detection coverage for compromised nodes while reducing overall processing complexity by concentrating analysis on the most indicative local patterns
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.