A framework for parsing configuration information from malware, extracting items like addresses, passwords, and filenames.
DC3-MWCP is a Python framework developed by the Defense Cyber Crime Center (DC3) for parsing configuration data from malware. It extracts structured information such as IP addresses, passwords, filenames, and mutexes, providing a standardized output format. The framework solves the problem of inconsistent malware analysis by enabling reusable, family-specific parsers that work across both manual and automated environments.
Malware analysts, reverse engineers, and cybersecurity professionals who need to extract and standardize configuration data from malware samples for threat intelligence or incident response.
Developers choose DC3-MWCP for its robust standardization, flexible integration options (CLI, REST API, Python API), and strong support for both ad-hoc analysis and large-scale automation. Its extensible schema and YARA matching capabilities make it a versatile tool for building consistent malware parsing pipelines.
DC3 Malware Configuration Parser (DC3-MWCP) is a framework for parsing configuration information from malware. The information extracted from malware includes items such as addresses, passwords, filenames, and mutex names.
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Defines a consistent JSON schema for malware configuration data, enabling comparable outputs across parsers and facilitating integration with threat intelligence platforms, as outlined in the Schema section.
Supports CLI, REST API, and Python API, allowing deployment in various workflows from manual analysis to automated pipelines, demonstrated in the Usage examples.
Provides utilities and guidelines for parser development, with support for custom metadata and easy sharing via entry points, as detailed in the Parser Development guide.
Automatically selects parsers based on YARA rule matches, enabling recursive analysis of unidentified files, though setup requires manual configuration of a YARA repository.
Requires configuring a YARA repository with specific meta fields, which can be complex and error-prone, as noted in the YARA Matching setup instructions.
Includes only a handful of parsers, necessitating custom development for most malware families, which increases initial effort and maintenance burden.
Recursive YARA matching and processing can slow down analysis, especially with large or numerous samples, impacting scalability in automated environments.