Ontology Mapping for Malware Analysis Standardization
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Solution Overview
Problem
Cybersecurity systems face overwhelming amounts of threat analyses from diverse sources, making it difficult to identify semantically equivalent malware for effective mapping and analysis, leading to inefficiencies in threat detection and response.
Innovation Solution
An ontology mapping system that employs fuzzy string matching algorithms and reference dictionaries to evaluate and filter malware analyses, grouping similar threats and providing a standardized 'smart attribute' for improved threat intelligence sharing across organizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If security analysts manually analyze threat analyses from multiple sources, then threat detection accuracy may improve, but the workload and time required increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational methods. The system uses fuzzy string matching algorithms and ontology mapping to automatically compare and group threat analyses from multiple sources, eliminating the need for manual review while maintaining detection accuracy.
Solution Approach 2:
The system performs self-service by automatically ingesting, processing, and organizing threat analyses without human intervention. The automated ontology mapping system independently compares threats across sources, groups similar ones, and creates standardized outputs, freeing analysts from routine tasks.
2Reliability
If security software processes large amounts of logs and events, then comprehensive security monitoring improves, but system usability deteriorates
Solution Approach 1:
The patent extracts meaningful patterns and groupings from the massive volume of logs and events. By using fuzzy string matching and ontology mapping, the system identifies and separates significant security events from noise, presenting only the relevant grouped information to users rather than overwhelming raw data.
Solution Approach 2:
The system merges similar security events and threats from multiple sources into unified groups. This consolidation reduces the number of individual items users must review while maintaining comprehensive monitoring coverage, as related events are combined into single actionable intelligence items.
3Quantity of substance
If threat analyses from multiple sources are collected, then threat intelligence comprehensiveness improves, but information organization difficulty increases
Solution Approach 1:
The patent changes the organizational parameters of threat intelligence data by mapping all analyses to a standardized ontology structure. This transformation reorganizes the volume of information according to consistent categories and relationships, making the data manageable and queryable despite the increased quantity.
Solution Approach 2:
The system creates a universal ontology framework that can organize threat intelligence from any source. This multi-functional mapping approach handles diverse data formats and sources through a single standardized structure, simplifying organization regardless of the volume or variety of incoming information.
4Productivity
If automated threat analysis systems are implemented, then processing speed improves, but accuracy in identifying semantically equivalent malware decreases
Solution Approach 1:
The patent replaces simple automated string matching with sophisticated fuzzy string matching algorithms that account for semantic equivalence. This advanced computational method maintains high accuracy by understanding the meaning behind malware names and descriptions while processing at automated speeds.
Solution Approach 2:
The system incorporates feedback mechanisms where the ontology mapping results are validated and refined. The fuzzy matching algorithm continuously learns from the context of threat analyses, improving its accuracy in identifying semantically equivalent malware over time while maintaining rapid processing.
Data Source
AI summary
An article of manufacture includes a non-transitory medium including machine-readable instructions. The instructions are to be read and executed by a processor. The instructions, when read and executed by the processor, to cause the processor to receive a malware analysis of a malware from a computer security source and receive other malware analyses. Each other malware analysis is of another malware from another computer security source. The instructions may further cause the processor to perform a fuzzy matching algorithm to quantify a similarity of the malware analyses, determine that the malware is a same malware as other malware based upon results of the fuzzy matching algorithm, and later take a same corrective action for malware based upon a receipt of the malware analysis.