Computer Alert String Clustering With Semantic Distances
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Solution Overview
Problem
Existing clustering algorithms for computer system alerts, such as machine learning algorithms, fail to accurately differentiate between semantically similar and dissimilar strings, leading to inefficient grouping and increased repetitive alerts, which hampers security analysis and response.
Innovation Solution
A tailored string distance calculation algorithm that considers semantically-similar parts in strings, using length, entropy, and specific matching characteristics, and weights to calculate distances, which are then used by clustering algorithms for more effective grouping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional distance functions (e.g., Levenshtein distance) are used for clustering alert strings, then the clustering process is simple and fast, but the accuracy of differentiation between semantically similar and dissimilar strings deteriorates
Solution Approach 1:
The patent segments alert strings into multiple components (e.g., timestamp, source, destination, action, object) and calculates distance for each component separately using appropriate metrics. This segmentation allows the system to handle semantic similarity more accurately by evaluating each component's contribution independently, resolving the contradiction between measurement precision and algorithm complexity.
Solution Approach 2:
The patent applies different distance calculation strategies to different parts of the alert string based on their semantic importance. For example, critical components like source and destination use stricter matching criteria while less critical components use more flexible matching. This local quality approach improves overall differentiation accuracy without uniformly increasing complexity across the entire string.
2Productivity
If clustering algorithms group all similar-looking strings together, then the number of clusters is reduced, but the quality of clustering deteriorates due to inability to distinguish meaningful differences
Solution Approach 1:
By segmenting alert strings into meaningful components and calculating component-level distances, the system can efficiently process alerts while maintaining high clustering quality. The segmentation enables parallel processing of components, improving productivity, while the component-specific distance metrics ensure meaningful differences are preserved in the clustering results.
Solution Approach 2:
The patent changes the parameters used for distance calculation from simple character-based metrics to semantically-aware metrics that consider the meaning and importance of different string components. This parameter change allows the system to achieve both high processing efficiency and high clustering quality by focusing computational resources on the most discriminative features.
3Reliability
If cautionary alert generation is used to ensure security coverage, then security risk detection improves, but the number of repetitive alerts increases
Solution Approach 1:
The patent implements a feedback mechanism where clustering results are used to adjust alert generation and processing. By analyzing clustered patterns, the system can identify true security threats from repetitive false positives, maintaining high detection coverage while reducing unnecessary alerts through iterative refinement based on clustering feedback.
Solution Approach 2:
The patent merges repetitive alerts that belong to the same security pattern into unified cluster representations. This combining approach preserves the security coverage by maintaining the underlying threat pattern while reducing the quantity of individual repetitive alerts, making the alert system both reliable and manageable.
Data Source
AI summary
Systems, methods, and software can be used to clustering strings. A pair of string elements are obtained. One or more first strings are separated into a set of first string terms. One or more second strings are separated into into a set of second string terms. One or more string term distances are generated based on the set of first string terms and the set of second string terms. A distance between the first string element and the second string element is generated an provide to a clustering algorithm to generate a plurality of clusters.