Outlier Network Activity Detection via Dynamic Behavioral Modeling
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Solution Overview
Problem
Existing outlier network activity detection systems rely on static threshold models that fail to adapt to evolving behaviors, leading to inefficiencies in identifying normal versus outlier behavior, and often miss new and emerging threats due to their inability to learn and update effectively.
Innovation Solution
A system that transforms network activity data into behavioral models to identify similarities among users, using graph-based analysis and decision engine logic to predict and mitigate outlier activity, allowing for real-time updates and integration of additional data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static threshold detection schemes are used, then the system is simple to implement, but the system cannot adapt to evolving outlier behaviors over time
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously learning from network activity patterns. The system adapts thresholds over time based on observed behaviors, transitioning from static to dynamic detection parameters that evolve with the network environment, thereby resolving the contradiction between implementation simplicity and adaptability.
Solution Approach 2:
The system incorporates feedback mechanisms where detection outcomes are fed back into the learning model to continuously refine threshold settings. This closed-loop approach allows the system to automatically adjust to new outlier patterns while maintaining operational simplicity through automated adaptation.
2Measurement precision
If thresholds are set too high, then false positive indications are reduced, but some outlier network activity may not be detected
Solution Approach 1:
The system dynamically adjusts detection thresholds based on learned network patterns rather than using fixed high thresholds. This allows the system to maintain high precision by adapting to normal behavior variations while preserving reliability through context-aware detection that responds to actual outlier patterns.
Solution Approach 2:
The patent changes the parameter values of thresholds dynamically based on learned patterns. By adjusting threshold parameters adaptively rather than maintaining static high values, the system achieves both low false positive rates and high outlier detection reliability through context-sensitive parameter modification.
3Reliability
If thresholds are set too low, then all outlier network activity is detected, but the system generates a lot of false positive indications
Solution Approach 1:
The system uses feedback from detection outcomes to learn and adjust threshold settings, preventing excessive false positives while maintaining reliable outlier detection. The feedback loop enables the system to identify true outlier patterns versus normal variations, resolving the contradiction between comprehensive detection and false positive reduction.
Solution Approach 2:
The patent implements adaptive parameter changes that modify threshold values based on learned patterns. This prevents the system from using overly sensitive low thresholds that generate false positives, while maintaining reliable detection through intelligent parameter adjustment based on contextual understanding of network behavior.
4Device complexity
If threshold detection systems are updated periodically, then the system maintains operational simplicity, but the updates are insufficient to keep pace with evolving outlier activities
Solution Approach 1:
The patent transitions from periodic static updates to continuous dynamic adaptation. The system continuously learns and adjusts to new outlier patterns in real-time, eliminating the lag inherent in periodic updates while maintaining operational simplicity through automated continuous learning processes.
Solution Approach 2:
The system implements continuous learning and adaptation rather than discrete periodic updates. This continuous action allows the system to keep pace with evolving threats by constantly refining its detection capabilities, resolving the contradiction between update frequency and adaptability to emerging threats.
5Area of stationary object
If the system focuses on network-level analysis, then broad patterns can be identified, but user-level behavioral nuances are missed
Solution Approach 1:
The patent segments the analysis into multiple levels: network-level broad patterns and user-level behavioral nuances. By dividing the analysis scope into hierarchical segments, the system simultaneously captures both macroscopic network patterns and microscopic user behavior characteristics, resolving the contradiction between analysis scope and detection precision.
Solution Approach 2:
The system adds a user-level dimension to the traditional network-level analysis. By analyzing behavior at multiple dimensional levels (network-wide and user-specific), the system achieves both broad pattern recognition and precise behavioral similarity detection through multi-dimensional analysis.
Data Source
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AI summary
Embodiments of systems and methods for identifying and mitigating outlier network activity are disclosed. In embodiments, network activity by a plurality of users may be monitored and, based on the monitoring, a plurality of data sets may be compiled. Each of the plurality data sets may include information representative of activity by the plurality of users. A network model representative of at least a portion of the network activity may be constructed based on one or more of the plurality of data sets. The network model may be evaluated against a set of rules to produce outputs that include at least one of: a set of classifications, a set of link metrics, and a set of communities. Decision engine logic may be executed against the outputs to identify outlier network activity. In response to identifying outlier network activity, operations to mitigate the identified outlier network activity may be executed.