Recursive Forensic Artifact Analysis for Threat Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current analytical software systems for IT security are inefficient and incomplete in analyzing and correlating forensic artifacts, often overlooking metadata, which hinders the identification of cybersecurity threats and wastes opportunities for threat intelligence.
Innovation Solution
A method and system for forensic artifact analysis that includes collecting and analyzing artifacts by recursively extracting embedded files, objects, and metadata, using multiple analysis modules such as signature matching, heuristic, machine learning, and deep learning, and storing results in a central intelligence database to trigger security actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current analytical software systems are used to compare artifacts against known threats, then basic threat detection is provided, but the analysis is time-consuming and metadata is overlooked
Solution Approach 1:
The patent segments the artifact analysis process into multiple parallel analysis modules (signature matching, heuristic analysis, machine learning, deep learning) that operate simultaneously on different aspects of the artifact and its metadata, reducing overall analysis time while maintaining comprehensive threat detection
Solution Approach 2:
The system performs preliminary extraction and classification of metadata from artifacts before the main analysis process. By pre-processing and organizing metadata structures in advance, the system prepares data for rapid comparison against threat intelligence databases, reducing the time required for actual threat detection
2Loss of information
If thorough analysis and correlation of metadata within files is performed, then security intelligence is improved, but the complexity of the analysis system increases
Solution Approach 1:
The patent divides the complex metadata analysis task into distinct modules: extraction module, classification module, correlation module, and analysis module. Each module handles a specific aspect of metadata processing, making the overall complex system manageable through functional segmentation while achieving comprehensive security intelligence
Solution Approach 2:
The patent introduces metadata structures as intermediary representations that organize and standardize extracted metadata before it undergoes correlation and analysis. These intermediary structures act as a bridge between raw artifact data and threat intelligence databases, simplifying the complexity of direct correlation while preserving security intelligence
3Loss of information
If files are broken down into smallest units to extract embedded files, objects, streams, and metadata, then threat intelligence collection is improved, but the processing complexity increases
Solution Approach 1:
The patent implements a nested processing approach where artifacts are recursively decomposed into embedded files, objects, and streams, which themselves may contain further nested elements. This systematic nesting strategy ensures complete threat intelligence collection by examining all levels of embedded content while managing processing complexity through structured recursion
Solution Approach 2:
The system performs preliminary identification and classification of embedded elements before extracting them. By pre-identifying the structure and types of embedded files, objects, and streams, the system prepares extraction paths in advance, reducing the complexity of actual extraction and analysis operations
Data Source
AI summary
A non-transitory computer-readable medium comprising instructions which cause a computer system to carry out a method for artifact metadata extraction and analysis. The method includes the steps of collecting an artifact from a source, determining whether the artifact has been previously analyzed; if the artifact has not been previously analyzed, identifying a type of the artifact, recursively extracting embedded artifacts and metadata within the artifact, testing to determine whether the artifact has reached minimal elements, continuing to recursively extract embedded artifacts and metadata if the artifact has not been fully broken down, analyzing all artifacts, embedded artifacts, and metadata, once extraction has been completed, for malicious content; storing the artifacts, embedded artifacts, metadata and analysis results in a central database; and triggering a security action if it is determined, that any of the original artifact, one or more embedded artifacts and metadata extracted from the artifact contain malicious content.