Cyber Threat Analysis Using AI for Variant Malware Prediction
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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 (APT) effectively, lacking standardized description methods for malware and attack techniques, which leads to delayed detection and confusion among experts.
Innovation Solution
A cyber threat information processing apparatus and method using machine learning techniques to identify malware, attackers, and predict attacks by integrating static and dynamic analysis, correlation analysis, and AI-based prediction, providing normalized and standardized cyber threat information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pattern-based detection methods are used, then detection speed and accuracy are improved for known threats, but detection capability deteriorates for new or variant malware
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing malware samples, attack techniques, and attacker information in advance to build comprehensive databases. This preliminary data collection and analysis enables the system to detect new and variant malware by comparing against known patterns and characteristics stored in the databases, rather than relying solely on real-time pattern matching.
Solution Approach 2:
The system introduces intermediary elements including AI analysis units that bridge pattern-based detection and new threat identification, databases that store intermediate analysis results, and standardized description frameworks that mediate between different analysis perspectives. These intermediaries enable the system to handle both known patterns and novel threats effectively.
2Productivity
If AI analysis is used to detect and analyze malware, then analysis capability is improved, but ability to address new or variant malware deteriorates due to lack of training data
Solution Approach 1:
The system performs preliminary data collection and analysis by gathering malware samples, attack techniques, and attacker information in advance to build comprehensive databases. This preliminary action provides the training data and reference information needed for AI analysis, enabling the system to maintain high productivity while adapting to new threats.
Solution Approach 2:
The system implements multi-functional databases that store diverse information including malware characteristics, attack techniques, and attacker profiles. These universal databases serve multiple purposes: training AI models, providing reference data for analysis, and enabling detection of various threat types, thereby improving both productivity and adaptability.
3Measurement precision
If individual case-focused detection methods are used, then detection of specific malware is improved, but prediction of future cyber threats deteriorates
Solution Approach 1:
The system performs preliminary analysis by collecting and analyzing malware samples, attack techniques, and attacker information in advance to build comprehensive databases. This preliminary action enables the system to identify patterns and trends that can predict future threats, rather than merely detecting individual cases.
Solution Approach 2:
The system implements feedback mechanisms by continuously analyzing detected threats, updating databases with new information, and refining detection models. This feedback loop enables the system to learn from individual cases and improve its ability to predict future threats, transforming isolated detections into predictive capability.
4Loss of information
If standardized description methods are implemented, then information normalization is improved, but system complexity increases due to standardization requirements
Solution Approach 1:
The system implements universal standardized description methods that serve multiple functions: normalizing information from different sources, enabling consistent analysis across diverse threats, and facilitating data exchange between systems. These multi-functional standards reduce information loss without proportionally increasing complexity by serving multiple purposes simultaneously.
Solution Approach 2:
The system applies parameter changes by transforming diverse malware and threat information into standardized parameters and formats. This parameter normalization enables consistent analysis and comparison across different threat types while using efficient data structures that minimize the complexity overhead of standardization.
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; and providing the processed cyber threat information to the user through a user interface.