Subparse | Modular Malware Analysis Artifact Collection And Correlation Framework


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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 Started

Software Requirements

To 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.

Software Status Link
Docker Required Installation Guide
Python3.8.1 Required Installation Guide
Pyenv Recommended Installation Guide

Additional Requirements

After 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 RequirementsDocker Requirements

Installation steps


Command Line Options

Command line options that are available for subparse/parser/

Argument Alternative Required Description
-h –help No Shows help menu
-d SAMPLES_DIR –directory SAMPLES_DIR Yes Directory of samples to parse
-e ENRICHER_MODULES –enrichers ENRICHER_MODULES No Enricher modules to use for additional parsing
-r –reset No Reset/delete all data in the configured Elasticsearch cluster
-v –verbose No Display verbose commandline output
-s –service-mode No Enters service mode allowing for mode samples to be added to the SAMPLES_DIR while processing

Viewing Results

To 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 Collected

Before 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 Modules

Parsers 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 Modules


Enricher Modules

These modules are optional modules that will ONLY get executed if specified via the -e | --enrichers flag on the command line.

Default Modules


Developing Custom Parsers & Enrichers

Subparse’s web view was built using Bootstrap for its CSS, this allows for any built in Bootstrap CSS to be used when developing your own custom Parser/Enricher Vue.js files. We have also provided an example for each to help get started and have also implemented a few custom widgets to ease the process of development and to promote standardization in the way information is being displayed. All Vue.js files are used for dynamically displaying information from the custom Parser/Enricher and are used as templates for the data.

Note: Naming conventions with both class and file names must be strictly adheared to, this is the first thing that should be checked if you run into issues now getting your custom Parser/Enricher to be executed. The naming convention of your Parser/Enricher must use the same name across all of the files and class names.


The logger object is a singleton implementation of the default Python logger. For indepth usage please reference the Offical Doc. For Subparse the only logging methods that we recommend using are the logging levels for output. These are:

  • debug
  • warning
  • error
  • critical
  • exception
  • log
  • info


  • This research and all the co-authors have been supported by NSA Grant H98230-20-1-0326.


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