Regularized Probabilistic Model for IP Geolocation Outlier Detection
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Solution Overview
Problem
Geolocation databases face challenges such as inaccurate coordinates and location aggregation to geographic centers, making outlier detection techniques problematic in characterizing Internet Protocol (IP) traffic, which is crucial for network security and fraud detection.
Innovation Solution
A regularized probabilistic model, specifically a Gaussian mixture model (GMM), is applied to IP network traffic to perform geolocation, detect outliers, and take remedial actions, addressing the geographic center assignment problem by restricting the number of clusters and weighting coordinates by the log of traffic events, thereby improving accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If geolocation databases are used to associate IP addresses with physical locations, then location information can be obtained, but the accuracy of coordinates deteriorates due to location aggregation to geographic centers
Solution Approach 1:
The patent segments the geolocation database into multiple hierarchical levels (country, region, city, postal code, neighborhood) rather than using a single aggregated geographic center. This segmentation allows the system to select the appropriate level of granularity based on the specific analysis needs, thereby maintaining location information availability while improving coordinate accuracy when needed.
Solution Approach 2:
The patent introduces a new dimension of analysis by using probabilistic models and outlier detection techniques that operate alongside traditional geolocation. Instead of relying solely on aggregated geographic centers, the system adds a statistical dimension that evaluates the reliability and accuracy of location data, enabling better decision-making about which location information to trust.
2Reliability
If outlier detection techniques are applied to detect geographically distant traffic origins, then network security can be improved, but the effectiveness deteriorates due to inaccurate coordinate data
Solution Approach 1:
The patent implements feedback mechanisms where the outlier detection system continuously monitors geolocation accuracy and adjusts its thresholds and parameters based on observed data patterns. By feeding back information about actual traffic patterns and detection results, the system adapts to compensate for coordinate inaccuracies, maintaining reliable security detection despite measurement precision limitations.
Solution Approach 2:
The patent dynamically changes detection parameters such as geographic distance thresholds, confidence levels, and clustering parameters based on the quality and reliability of available geolocation data. When coordinate accuracy is low, the system adjusts parameters to be more tolerant of variations, preventing false positives while still detecting genuine outliers, thus maintaining security reliability despite precision limitations.
3Measurement precision
If a probabilistic model is applied to perform geolocation, then geolocation accuracy can be improved, but computational complexity increases
Solution Approach 1:
The patent applies partial probabilistic modeling by using simplified probability distributions and selective application of complex models only where needed. Instead of applying full probabilistic models to all geolocation data, the system uses them selectively for outlier detection and validation, achieving improved accuracy where it matters most while limiting computational complexity in routine operations.
Data Source
AI summary
A method, computer-readable medium, and apparatus for classifying IP network traffic are disclosed. For example, a method may include a processor for applying a regularized probabilistic model to internet protocol network traffic for performing geolocation, wherein the internet protocol network traffic contains internet protocol address information, detecting an outlier in the internet protocol network traffic, wherein the outlier is associated with a transaction over a network, and performing a remedial action to address the outlier.