Cyber Threat Information Processing with NLP for New Malware
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Solution Overview
Problem
Existing cybersecurity technologies struggle to detect and respond to new or variant malware, decoy information, and advanced persistent threats, lacking standardization in malware description and attacker identification, and are ineffective in predicting future cyber threats.
Innovation Solution
A cyber threat information processing apparatus and method utilizing natural language processing, machine learning, and AI to identify malware, attackers, and predict future attacks, providing normalized and standardized cyber threat information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pattern-matching detection methods are used, then detection speed and accuracy are improved for known malware, but detection capability deteriorates for new or variant malware without established patterns
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing malware samples, attacker infrastructure data, and attack patterns in advance to build comprehensive threat intelligence databases. This enables the system to detect new malware variants by comparing them against known attacker patterns and infrastructure, even when exact pattern matches don't exist
Solution Approach 2:
The system introduces intermediary elements including natural language processing layers that translate technical cyber threat data into standardized, interpretable formats, and multi-layered analysis frameworks that bridge pattern-matching detection and AI-based behavioral analysis. These intermediaries enable seamless transition between detecting known patterns and identifying new threats
2Productivity
If AI analysis is used to detect and analyze malware, then analysis capability is improved, but fundamental technology to counter cybersecurity threats remains lacking
Solution Approach 1:
The system combines multiple analytical approaches into a composite detection framework: pattern-matching algorithms for known threats, AI-based behavioral analysis for anomaly detection, natural language processing for threat intelligence extraction, and correlation engines for contextualizing findings. This composite approach maintains high productivity while establishing reliable fundamental countermeasure capabilities
Solution Approach 2:
The system creates universal, standardized technologies for cyber threat detection including normalized data schemas, standardized threat classification frameworks, and multi-purpose analysis engines that can handle diverse threat types (malware, phishing, APTs) through unified methodologies, making fundamental countermeasure technology applicable across different security contexts
3Adaptability or versatility
If decoy information or fake information is used to deceive detection systems, then attacker success rate is improved, but detection system reliability deteriorates due to confusion
Solution Approach 1:
The system implements feedback mechanisms where detected decoy information and fake data are analyzed to understand attacker deception patterns. This feedback is then used to refine detection algorithms, improve pattern recognition accuracy, and update threat intelligence databases, thereby maintaining reliability even when attackers use sophisticated deception techniques
Solution Approach 2:
The system converts harmful decoy information and fake data into beneficial intelligence by analyzing these deceptive elements to identify attacker patterns, tactics, and infrastructure. Rather than being misled, the system uses decoys as additional data points to improve detection capabilities and understand attacker methodologies
4Adaptability or versatility
If malware detection names and attack techniques are not unified, then flexibility in description is improved, but identification accuracy and standardization deteriorate
Solution Approach 1:
The system changes parameters by implementing standardized naming conventions and classification frameworks for malware and attack techniques. These standardized parameters enable precise identification and consistent classification across different threats while maintaining the ability to describe diverse attack vectors through a unified taxonomy
Data Source
AI summary
Provided is a cyber threat information processing method including receiving input of a file or information on the file from a user through at least one interface; processing cyber threat information related to the received or input file or the information on the file; providing the processed cyber threat information to the user through a user interface; and performing natural language processing on the processed cyber threat information.