Malware Grouping via Artifact Clustering for Threat Detection
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Solution Overview
Problem
Existing security approaches fail to effectively identify and address advanced, unknown malware threats, such as zero-day threats and Advanced Persistent Threats (APTs), which evade traditional detection methods and pose significant risks to networks.
Innovation Solution
A malware analysis platform for threat intelligence that processes log files from dynamic and static analysis to identify high-risk artifacts, generates enhanced views of malware analysis results, and automatically groups malware samples based on artifacts, enabling the generation of new signatures for detection and blocking malicious activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection methods are used, then device complexity is reduced, but the ability to detect advanced malware threats deteriorates
Solution Approach 1:
The system segments malware analysis into multiple independent components: artifact extraction module, artifact grouping module, signature generation module, and detection module. Each component processes specific aspects of malware analysis independently, improving detection capability while managing system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by pre-extracting artifacts from malware samples, pre-grouping them into families based on shared characteristics, and pre-generating signatures before actual detection is needed. This prepares detection data in advance, enhancing real-time detection capability without increasing operational complexity.
2Productivity
If automated malware analysis is performed, then productivity is improved, but loss of information increases due to the volume of data generated
Solution Approach 1:
The system extracts only the most relevant and distinctive artifacts from malware analysis data, such as unique file paths, registry keys, and configuration parameters. By selectively extracting critical information rather than processing all generated data, the system maintains high analysis speed while preventing information overload.
Solution Approach 2:
Instead of analyzing entire malware samples directly, the system inverts the approach by first extracting discrete artifacts and then analyzing these extracted elements. This inversion transforms unmanageable large-scale data into manageable discrete units, maintaining productivity while reducing information loss.
3Measurement precision
If manual malware analysis is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system implements self-service through automated artifact extraction, automatic artifact grouping into malware families, and automated signature generation. These self-service capabilities maintain high analysis accuracy that previously required manual expertise while dramatically reducing analysis time by eliminating manual intervention.
Solution Approach 2:
The system replaces manual mechanical analysis processes with automated computational mechanisms. Algorithms automatically extract artifacts, group them by similarity, and generate detection signatures, substituting human manual analysis with automated systems that achieve comparable or superior precision at much higher speeds.
Data Source
AI summary
Techniques for automatically grouping malware based on artifacts are disclosed. In some embodiments, a system, process, and/or computer program product for automatically grouping malware based on artifacts includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract features associated with malware; clustering the plurality of samples based on the extracted features; and performing an action based on the clustering output.