Automated Malware Clustering via URL and Feature Analysis
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Solution Overview
Problem
Current malware analysis systems require manual vetting of sandbox reports, which is time-consuming due to the presence of false positives and the need to analyze extensive lists of URLs, and existing clustering methods are inefficient, especially with mixed data types and computationally expensive malware graphs.
Innovation Solution
An automated malware analysis system that uses soft clustering of URLs based on text encoding and auto-encoder-based feature extraction, allowing for probabilistic clustering with Gaussian mixture models, and subsequently clusters sandbox reports using density-based spatial clustering, enabling efficient decision support for security analysts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual vetting of sandbox reports is performed, then analysis accuracy is maintained, but time consumption increases significantly
Solution Approach 1:
The system segments the large set of sandbox reports into multiple clusters based on similarity metrics. Each cluster contains reports with comparable characteristics, allowing analysts to review one representative report per cluster rather than every individual report, thereby reducing time consumption while maintaining analysis accuracy through focused human verification of cluster representatives
Solution Approach 2:
The system performs automated preliminary analysis and clustering of sandbox reports using machine learning algorithms. This self-service capability handles the initial processing and grouping of reports, filtering out obvious patterns and organizing data structure, so that analysts only need to perform final verification on clustered results rather than manual vetting of every report from scratch
2Ease of manufacture
If lexical features extraction method is used for clustering, then implementation simplicity is achieved, but clustering accuracy deteriorates due to mixed data types
Solution Approach 1:
The system transforms mixed data types (continuous and binary features) into a unified representation suitable for clustering. By applying parameter transformations and using distance metrics that can handle mixed types (such as Gower distance), the system maintains both implementation feasibility and clustering accuracy, overcoming the limitation of traditional lexical feature extraction methods
3Measurement precision
If malware graphs are used for clustering, then representation accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The system extracts key clustering features from sandbox reports without constructing complete malware graphs. By selecting and extracting only the most relevant features (such as URL patterns, file indicators, and behavioral characteristics) that are sufficient for clustering purposes, the system achieves good representation accuracy while avoiding the high computational cost of building and processing full malware graphs
4Productivity
If automated clustering of sandbox reports is implemented, then productivity is improved, but handling of false positives becomes more complex
Solution Approach 1:
The system incorporates feedback mechanisms where analyst decisions on clustered reports are fed back into the clustering model. When analysts verify or correct cluster assignments, this feedback is used to refine and retrain the clustering algorithms, improving their ability to distinguish true positives from false positives over time. This iterative feedback process maintains high productivity while reducing the complexity of false positive handling through progressively smarter automation
Data Source
AI summary
A system and a method for automatically clustering sandbox analysis reports of similar malware samples. An automated malware analysis process includes receiving from a sandbox server the sandbox analysis reports of the similar malware samples at an application programming interface (API) of the clustering server, clustering similar Uniform Resource Locators (URLs) together and clustering the sandbox analysis reports of events in sandbox reports clusters (1-n) based on the URL clustering, static properties of the malware samples and dynamic properties of the malware samples.