Cybercriminal Communication Data Extraction and Classification
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Solution Overview
Problem
Current solutions for analyzing cybercriminal communication data from the dark web are labor-intensive, produce incomplete results, and fail to provide a comprehensive overview of cyber attacks, including pre-, peri-, and post-attack intelligence, due to reliance on manual analysis and lack of efficient data mining capabilities.
Innovation Solution
A cybersecurity intelligence system that extracts, classifies, and ranks cybercriminal communication data using a taxonomy of artifacts, incorporating Natural Language Processing and keyword lists to identify threat topics and actors, and integrates enriched data into cybersecurity intelligence exchange databases for enhanced threat identification and mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to analyze cybercriminal communication data, then analysis depth can be achieved, but the analysis process becomes labor-intensive and inefficient
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational systems including machine learning models, natural language processing algorithms, and data mining techniques. These systems automatically extract, classify, and analyze cybercriminal communication data from dark web forums, eliminating labor-intensive manual processes while maintaining or improving analysis depth through sophisticated pattern recognition and threat intelligence generation.
2Loss of information
If comprehensive data collection from dark web forums is performed, then complete threat intelligence is achieved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modular components: data collection modules that gather information from multiple dark web forums, preprocessing modules that clean and normalize data, analysis modules that apply different machine learning techniques, and output modules that generate threat intelligence reports. This segmentation manages processing complexity while ensuring comprehensive threat intelligence coverage through systematic handling of diverse data sources.
Solution Approach 2:
The patent introduces intermediary processing layers including data normalization standards, standardized threat classification taxonomies, and intermediate representation formats that bridge raw dark web data and final threat intelligence outputs. These intermediaries simplify complex data processing by providing structured transformation rules and standardized interfaces between different processing stages.
3Productivity
If automated analysis systems are implemented, then analysis efficiency improves, but implementation complexity and resource requirements increase
Solution Approach 1:
The patent designs a universal automated analysis platform that performs multiple functions through integrated modules: data collection from various dark web sources, natural language processing, entity extraction, threat classification, risk assessment, and report generation. This multi-functional system improves analysis efficiency across different threat types while managing implementation complexity through standardized architectures and reusable components that can be configured for specific analysis needs.
Data Source
AI summary
An apparatus, including systems and methods, for classifying, mapping, and predicting cybercriminal activity is disclosed herein. For example, in some embodiments, an apparatus is configured to: receive cybercriminal communication (CCC) data of postings from a source forum; identify, classify, and rank a threat topic for each posting; identify a first subset of postings that includes postings assigned the threat topic classification with the greatest threat topic rank; for each posting of the first subset of postings: identify and rank the threat actor; identify a second subset of postings that includes postings associated with the threat actor assigned the greatest threat actor rank; and send, to a cybersecurity data exchange module, the CCC data of the second subset of postings and associated enriched data including the source forum, the threat topic classifications, the threat actor, the threat actor rank, or the other threat actors that mentioned the threat actor.