Contextual Data Processing Framework for Threat Intelligence
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Solution Overview
Problem
Existing threat detection solutions are often obsolete and too rigid, focusing on unimportant details rather than understanding context and relationships, leading to instability, security issues, and inefficiencies in recognizing and addressing complex threats.
Innovation Solution
A Contextual Data Processing Framework that integrates with network infrastructures to gather, process, and contextualize data, discovering relationships and classifying threats using multiple engines and machine learning/AI, enabling enhanced threat detection and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional wide-detection rules are used for threat detection, then detection coverage is maintained, but detection precision and context understanding deteriorate
Solution Approach 1:
The patent segments the threat detection process into multiple specialized engines (data processing engine, format recognition engine, multiple data handlers for different file types, context detection engine, classification engine). Each engine handles specific aspects of analysis, allowing precise detection of particular threat indicators while maintaining overall context understanding through their coordinated interaction.
Solution Approach 2:
The patent applies local quality by implementing specialized data handlers for different file types (PDF, Office documents, archives, etc.), each with tailored analysis capabilities. The context detection engine applies feature-based rules specifically designed for particular threat patterns, and the classification engine uses customized rules for different threat categories, enabling precise local analysis rather than uniform broad detection.
2Reliability
If multiple data handlers and engines are integrated for comprehensive analysis, then threat detection capability is improved, but system complexity increases
Solution Approach 1:
The system divides complex threat detection into segmented functional engines, each with a specific role. This modular architecture improves reliability by allowing each component to be optimized and tested independently while maintaining overall system functionality through standardized interfaces between engines.
Solution Approach 2:
The patent implements universality through a centralized data processing architecture where multiple specialized handlers (PDF handler, Office document handler, archive handler, etc.) interface with common engines (format recognition engine, context detection engine, classification engine). This multi-functional design allows the system to handle diverse file types and threat patterns through a unified framework, managing complexity through standardized interaction protocols.
3Adaptability or versatility
If traditional rigid detection methods are used, then system stability is maintained, but adaptability to new threats deteriorates
Solution Approach 1:
The patent implements dynamics through machine learning models that continuously learn from new threat data and adapt their detection parameters. The system dynamically updates threat indicators, modifies detection rules based on emerging patterns, and adjusts classification thresholds in response to new threat types, enabling continuous adaptation while maintaining operational stability through controlled update mechanisms.
Solution Approach 2:
The system employs parameter changes by allowing detection thresholds, feature weights, and classification criteria to be dynamically adjusted based on learned patterns from training data and emerging threats. The machine learning components modify detection parameters adaptively, enabling the system to respond to new threat landscapes while maintaining stable core functionality through managed parameter evolution.
4Measurement precision
If comprehensive data processing and contextualization is performed, then threat recognition accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action through pre-processing steps including format recognition that identifies file types before detailed analysis, and pre-defined feature extraction templates for common file formats. The system prepares detection rules and classification criteria in advance, and uses cached results from previous analyses of similar files, reducing redundant processing while maintaining comprehensive threat recognition accuracy.
Solution Approach 2:
The system implements partial action by applying different levels of analysis depth based on initial screening results. Not all files undergo the complete multi-engine analysis pipeline - the format recognition engine and initial handlers perform triage to identify files requiring full contextual analysis versus those that can be processed with simpler rules, reducing average processing time while maintaining high accuracy for actual threats.
Data Source
AI summary
A locally or remotely executing Contextual Data Processing Framework or plugin can be integrated with existing network infrastructures to enhance threat detection, intelligence, and remediation solutions. The Contextual Data Processing Framework can be deployed within the local infrastructure with one or more computing devices on one or more networks or may operate as a Software as a Service (SaaS) on a remote service for the local infrastructures. The Contextual Data Processing Framework leverages multiple stages that involve gathering local infrastructure data, processing, scanning, and contextualizing the gathered data, discovering relationships with other data, and then classifying data objects within recognized context and generating reports as necessary. The Contextual Data Processing Framework can be integrated with machine learning (ML) or artificial intelligence (AI) solutions to learn and automate the decisive processes.