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Subparse : Modular Malware Analysis Artifact Collection And Correlation Framework

Subparse, is a modular framework developed by Josh Strochein, Aaron Baker, and Odin Bernstein. The framework is designed to parse and index malware files and present the information found during the parsing in a searchable web-viewer. The framework is modular, making use of a core parsing engine, parsing modules, and a variety of enrichers that add additional information to the malware indices.

The main input values for the framework are directories of malware files, which the core parsing engine or a user-specified parsing engine parses before adding additional information from any user-specified enrichment engine all before indexing the information parsed into an elasticsearch index.

The information gathered can then be searched and viewed via a web-viewer, which also allows for filtering on any value gathered from any file. There are currently 3 parsing engine, the default parsing modules (ELFParser, OLEParser and PEParser), and 4 enrichment modules (ABUSEEnricher, CAPEEnricher, STRINGEnricher and YARAEnricher). Getting StartedSoftware RequirementsTo get started using Subparse there are a few requrired/recommened programs that need to be installed and setup before trying to work with our software.
SoftwareStatusLinkDockerRequiredInstallation GuidePython3.8.1RequiredInstallation GuidePyenvRecommendedInstallation Guide Additional RequirementsAfter getting the required/recommended software installed to your system there are a few other steps that need to be taken to get Subparse installed. Python Requirements Python requires some other packages to be installed that Subparse is dependent on for its processes. To get the Python set up completed navigate to the location of your Subparse installation and go to the *parser* folder. The following commands that you will need to use to install the Python requirements is: Docker Requirements Since Subparse uses Docker for its backend and web interface, the set up of the Docker containers needs to be completed before being able to use the program. To do this navigate to the root directory of the Subparse installation location, and use the following command to set up the docker instances: Installation steps* Installation Steps UsageCommand Line OptionsCommand line options that are available for subparse/parser/subparse.py:
ArgumentAlternativeRequiredDescription-h–helpNoShows help menu-d SAMPLES_DIR–directory SAMPLES_DIRYesDirectory of samples to parse-e ENRICHER_MODULES–enrichers ENRICHER_MODULESNoEnricher modules to use for additional parsing-r–resetNoReset/delete all data in the configured Elasticsearch cluster-v–verboseNoDisplay verbose commandline output-s–service-modeNoEnters service mode allowing for mode samples to be added to the SAMPLES_DIR while processing Viewing ResultsTo view the results from Subparse’s parsers, navigate to localhost:8080. If you are having trouble viewing the site, make sure that you have the container started up in Docker and that there is not another process running on port 8080 that could cause the site to not be available. General Information CollectedBefore any parser is executed general information is collected about the sample regardless of the underlying file type. This information includes:

* MD5 hash of the sample
* SHA256 hash of the sample
* Sample name
* Sample size
* Extension of sample
* Derived extension of sample Parser ModulesParsers are ONLY executed on samples that match the file type. For example, PE files will by default have the PEParser executed against them due to the file type corresponding with those the PEParser is able to examine. Default ModulesELFParser This is the default parsing module that will be executed against ELF files. Information that is collected: OLEParser This is the default parsing module that will be executed[...]

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