Hierarchical Clustering for Network Security Analysis
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Solution Overview
Problem
Traditional clustering methods are inefficient in handling data points with multiple dimensions, often requiring excessive computational resources and time, which limits their ability to provide accurate insights, particularly in network security analysis.
Innovation Solution
A hierarchical clustering method that performs multiple passes of k-means clustering, starting with first-level cluster centroids and iteratively determining second-level and additional cluster centroids based on average distance thresholds, to accurately predict user behavior and reduce computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clustering methods (e.g., k-means) are used to cluster data points with multiple dimensions, then computational efficiency is maintained, but clustering accuracy deteriorates
Solution Approach 1:
The patent segments the clustering process into multiple hierarchical levels. First-level cluster centroids are determined using all data points, then second-level centroids are determined using only data points assigned to specific first-level centroids. This segmentation allows the use of computationally efficient methods at each level while achieving better overall clustering accuracy through the hierarchical structure.
2Measurement precision
If more sophisticated clustering methods are used to improve clustering accuracy, then measurement precision improves, but computing resources and time consumption increase
Solution Approach 1:
The patent performs preliminary clustering at the first level to assign data points to initial centroids. This preliminary action creates a structured foundation that enables subsequent second-level clustering to focus only on relevant subsets of data, thereby achieving high accuracy without proportionally increasing overall processing time.
3Measurement precision
If hierarchical clustering with multiple levels is implemented, then clustering accuracy improves, but device complexity increases
Solution Approach 1:
The algorithm is segmented into distinct first-level and second-level clustering stages, each with clearly defined inputs and outputs. This segmentation makes the complex hierarchical process more manageable and implementable by breaking it into discrete, executable steps that can be implemented systematically.
Data Source
AI summary
In one embodiment, a method includes receiving data points, determining first-level cluster centroids using the data points, and assigning each data point to at least one first-level cluster centroid. The method also includes determining second-level cluster centroids using data points assigned to a particular first-level cluster centroid and assigning each of the data points previously assigned to the particular first-level cluster centroid to at least one second-level cluster centroid. The method further includes determining clusters based on the first-level cluster centroids and the second-level cluster centroids, and assigning each data point to at least one cluster.