Unsupervised Learning Efficacy Measures for Cybersecurity Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Measuring the efficacy of unsupervised machine learning in cybersecurity is challenging due to the lack of labeled data, making it difficult to determine the effectiveness of clustering and the impact of different learning parameters on groupings.
Innovation Solution
The introduction of efficacy measures such as attack rate, thwart rate, and Thwart Score, which assess the security of user information clustering by calculating distance metrics like Levenshtein distance, helps in determining the optimal number of clusters and improving the unsupervised learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unsupervised learning is performed with multiple clustering parameters to improve user identification accuracy, then the security and efficacy of user information protection is improved, but the computational complexity and time required for processing increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating distance metrics between all pairs of data points in the initial dataset before performing unsupervised learning. This preprocessing step stores the distance information, allowing the clustering algorithm to efficiently determine optimal cluster assignments without recalculating distances during multiple parameter iterations, thus reducing computational complexity while maintaining identification accuracy
Solution Approach 2:
The patent creates data representations (copies) of user information in clustered form that capture the essential patterns without storing all original data. These clustered data representations serve as simplified models that maintain security efficacy while reducing the computational burden of processing raw data repeatedly across different clustering scenarios
2Measurement precision
If the number of clusters is increased to improve the granularity of user information grouping, then the accuracy of user identification is improved, but the storage requirements and processing time increase
Solution Approach 1:
The patent segments user information into clustered groups based on similarity metrics, organizing data into meaningful segments that balance granularity with storage efficiency. By grouping similar data points together, the system achieves precise user identification through cluster assignments while storing only cluster assignments and representative data rather than all individual data points
Solution Approach 2:
The patent employs parameter changes by evaluating multiple clustering scenarios with different numbers of clusters and distance metric thresholds. This allows the system to identify the optimal clustering configuration that achieves sufficient identification accuracy with minimal storage requirements, avoiding unnecessary granularity that would increase storage burden
3Reliability
If efficacy measures are calculated using multiple distance metric thresholds to assess clustering quality, then the reliability of security assessment is improved, but the time required for evaluation increases
Solution Approach 1:
The patent applies partial action by calculating efficacy measures using a selected range of distance metric thresholds rather than exhaustively evaluating all possible threshold values. This approach identifies the critical threshold ranges that provide sufficient security assessment reliability without performing unnecessary calculations at extreme or redundant threshold levels, thus reducing evaluation time while maintaining assessment quality
Data Source
AI summary
A method for performing searches of user information comprises: receiving an initial dataset including the user information; calculating distance metrics between pairs of data in the initial dataset; performing unsupervised learning on the initial dataset to obtain a plurality of total number of clusters, each total number of clusters covering the initial dataset; determining efficacy measures for each total number of clusters using a plurality of distance metric thresholds; determining a desired efficacy measure in the efficacy measures, the desired efficacy measure corresponding to a desired distance metric threshold in the plurality of distance metric thresholds; determining a desired total number of clusters in the plurality of total number of clusters, the desired total number of clusters corresponding to the desired distance metric threshold; and performing unsupervised learning on the initial dataset using the desired total number of clusters to obtain a number of data representations of the user information.