Malware Data Clustering for Fraud Investigation Efficiency
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Solution Overview
Problem
Current systems for fraud investigation are inefficient due to the need for manual repetition of data searches across large datasets, leading to time-consuming and resource-intensive processes, and struggle to prioritize investigations effectively due to insufficient information from individual data items.
Innovation Solution
A data analysis system that automatically generates memory-efficient clustered data structures, allowing for automated analysis and scoring of these clusters, providing a human-readable summary and interactive interface to efficiently evaluate and prioritize data clusters related to fraud investigations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data search and analysis is performed across large datasets, then comprehensive investigation coverage is achieved, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent segments the large dataset into multiple clusters based on shared attributes and relationships. Each cluster represents a group of related data items that can be analyzed independently, allowing parallel processing and reducing overall analysis time while maintaining comprehensive coverage.
Solution Approach 2:
The system performs preliminary clustering and scoring actions before the analyst begins detailed investigation. Data items are pre-grouped into clusters and assigned priority scores based on their relationships and attributes, so that when the analyst starts work, the data is already organized and prioritized, eliminating the need for manual sifting through entire datasets.
2Reliability
If all data items in large datasets are loaded into memory for analysis, then complete data availability is achieved, but memory consumption becomes prohibitive
Solution Approach 1:
The dataset is divided into multiple clusters that can be loaded into memory individually or in manageable batches. Each cluster contains only the data items and relationships relevant to that specific group, dramatically reducing the memory footprint compared to loading the entire dataset at once, while still providing complete data availability for each cluster's analysis.
Solution Approach 2:
The system extracts and loads only the necessary cluster data into memory when needed, rather than loading all data. Each cluster can be independently extracted from the larger dataset, loaded into memory for analysis, and then discarded, allowing efficient use of memory resources while maintaining access to complete cluster data.
3Measurement precision
If individual data items are analyzed in isolation, then detailed item-level analysis is achieved, but relevant relationships and patterns are missed
Solution Approach 1:
The patent merges individual data items into clusters based on their relationships and shared attributes. Each cluster combines multiple related data items while preserving the individual item details, allowing analysts to examine both the granular item-level information and the broader relationship patterns simultaneously within a unified cluster context.
4Productivity
If automated clustering and scoring systems are implemented, then analysis efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs automated clustering and scoring operations without requiring complex manual configuration or intervention. The clustering algorithms and scoring mechanisms operate autonomously, automatically grouping data items and assigning priority scores based on predefined criteria, thereby improving analysis efficiency while keeping the operational complexity manageable through self-service automation.
Data Source
AI summary
Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyzes (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.