Cyber Threat Processing for Variant Malware and Attack Group Attribution
Find Innovative SolutionsGenerate Solutions
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 methods for describing malware and attack techniques, and failing to identify attackers accurately.
Innovation Solution
A cyber threat information processing apparatus and method that utilizes machine learning and AI to process cyber threat information, providing standardized and normalized information on attack groups, including attack names, routes, targets, tools, and timelines, capable of identifying malware, attack techniques, and attackers in real-time, even for variants, and predicting future attacks.
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 malware, but detection capability is lost for new or variant malware
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing malware samples, their behaviors, and attack patterns in advance to build comprehensive threat profiles. This includes gathering static analysis data, dynamic execution traces, and correlation information about attack groups and techniques before actual attacks occur, enabling faster response to new threats.
Solution Approach 2:
The system transitions from traditional single-dimension pattern matching to multi-dimensional analysis by integrating static analysis, dynamic analysis, and correlation analysis across multiple layers. This includes analyzing code characteristics, execution behaviors, network activities, and attacker attribution data simultaneously, creating a holistic threat detection framework that captures new and variant malware.
2Productivity
If AI-based malware analysis is used, then analysis capability is improved, but fundamental technology to counter cybersecurity threats is still lacking
Solution Approach 1:
The system implements a universal threat detection framework that handles multiple threat types (malware, APT, zero-day attacks) and analysis modes (static, dynamic, correlation) through a unified architecture. The same core infrastructure supports diverse analysis tasks, from simple virus detection to complex attacker attribution, eliminating the need for separate specialized systems.
Solution Approach 2:
The system introduces standardized threat information models and normalization layers as intermediaries between raw data and analysis engines. These standardized representations of threats, attackers, and techniques enable different analysis components to work together seamlessly and provide a foundation for reliable threat countermeasures across various AI-based analysis tools.
3Measurement precision
If individual case-focused detection method is used, then specific malware detection is improved, but prediction capability for future attacks is reduced
Solution Approach 1:
The system implements feedback mechanisms where detection results, analysis data, and threat intelligence are continuously fed back into the learning and prediction systems. This creates a closed-loop system where each detected threat improves future detection and prediction capabilities, allowing the system to learn from past attacks and better predict future threats while maintaining accurate identification of specific malware.
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, wherein the provided cyber threat information includes information on an attack group.