Cyber Threat Information Processing via Natural Language Models
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Solution Overview
Problem
Existing cybersecurity technologies struggle to detect and respond to new or variant malware, as well as advanced persistent threat (APT) attacks, due to limitations in pattern recognition and analysis, leading to delayed detection and confusion in identifying attackers and attack techniques.
Innovation Solution
A cyber threat information processing apparatus and method that utilizes natural language processing and machine learning to analyze and identify malware, attack techniques, and attackers, even for variant malware, by processing cyber threat information related to input files and providing standardized and normalized information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional pattern-based detection methods are used, then detection speed is improved for known threats, but detection capability deteriorates for new or variant malware
Solution Approach 1:
The system dynamically adapts its analysis approach based on the threat being detected. For known threats, it uses rapid pattern matching; for new or variant malware, it automatically employs more comprehensive static and dynamic analysis methods, allowing the detection system to flexibly adjust its behavior to match the nature of the threat encountered
Solution Approach 2:
The system changes multiple parameters simultaneously including analysis depth, data collection scope, and processing methods. It transitions from shallow pattern matching to deep static and dynamic analysis when dealing with new threats, fundamentally altering how the system processes and evaluates malware samples
2Measurement precision
If AI-based malware analysis is used, then analysis capability is improved, but response time deteriorates due to lack of fundamental countermeasure technology
Solution Approach 1:
The system performs preliminary actions by pre-collecting and organizing vast amounts of malware data, attack patterns, and threat intelligence before actual detection is needed. This preparatory work enables faster response times during actual threats while maintaining high analysis capability through pre-processed information
Solution Approach 2:
The system creates a universal framework that handles multiple types of threats (new malware, variant malware, APT attacks) using integrated methods. It combines pattern recognition, static analysis, dynamic analysis, and threat intelligence into a single multi-functional system that can respond to diverse threats efficiently
3Measurement precision
If standardized cyber threat information processing is implemented, then information accuracy is improved, but processing complexity increases
Solution Approach 1:
The system applies homogeneity by standardizing the processing of cyber threat information across all threat types. It uses uniform data structures, consistent analysis methodologies, and standardized output formats for malware detection, attack technique identification, and attacker profiling, making complex processing manageable through consistent patterns
Data Source
AI summary
Provided is a cyber threat information processing method including receiving a CTI analysis request for a file from a client; analyzing the file to obtain analysis information of the CTI for the file; generating a CTI query related to the file based on the analyzed CTI and delivering the CTI query to a natural language model; and providing natural language description information according to the CTI query obtained from the CTI for the analyzed file and the natural language model to the client as visualization information based on a Web service.