Cyber Threat Clustering for Variant Malware Detection
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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 that utilizes opcode-and-ASM code combinations, clustering, and ensemble machine learning to identify and predict malware, attack techniques, and attackers, even for variants, through preprocessing, static and dynamic analysis, and correlation analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern matching technology is used to detect malware, 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 in advance to establish baseline patterns and characteristics. This includes gathering training data from multiple sources, performing initial analysis to identify common features, and preparing reference databases before encountering new threats, enabling faster response to variant malware
Solution Approach 2:
The system changes parameters by transitioning from static pattern matching to dynamic analysis that examines multiple dimensions of malware behavior. This includes analyzing code structure, execution patterns, network communication, and system interactions simultaneously, allowing the system to detect new malware variants by comparing their behavioral parameters against established threat profiles
2Productivity
If AI analysis is used to detect malware, then analysis capability is improved, but fundamental countermeasure technology remains lacking and decoy information causes confusion
Solution Approach 1:
The system introduces an intermediary layer between AI analysis and detection decisions. This intermediary performs verification by cross-checking AI-generated findings against multiple independent analysis methods, validating results through consistency checks, and filtering out false positives caused by decoy information before final detection conclusions are reached
Solution Approach 2:
The system implements feedback mechanisms where detection results and analysis outcomes are continuously fed back into the training database. This allows the system to learn from both successful detections and cases where decoys caused confusion, progressively improving its ability to distinguish genuine threats from deceptive information through iterative refinement
3Measurement precision
If standardized threat information processing is implemented, then threat identification accuracy is improved, but information processing complexity increases due to multiple analysis dimensions
Solution Approach 1:
The system segments the complex threat information processing into distinct modular components: data collection modules, analysis modules (static and dynamic), correlation modules, and reporting modules. Each module handles specific aspects of analysis independently, processing standardized threat information through specialized functions that can be executed in sequence or parallel, reducing overall system complexity while maintaining comprehensive analysis capability
Data Source
AI summary
A cyber threat information processing method, a cyber threat information processing apparatus, and a storage medium storing a cyber threat information processing program may analyze and process an executable file, perform clustering to generate one or more clusters, and determine similarity with a cluster of another user based on characteristic information of the executable file.