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. A parser module is usually created per malware family. DC3-MWCP is designed to help ensure consistency in parser function and output, ease parser development, and facilitate parser sharing. DC3-MWCP supports both analyst directed analysis and large-scale automated execution, utilizing either the native python API, a REST API, or a provided command line tool. DC3-MWCP is authored by the Defense Cyber Crime Center (DC3).
- Parser Development
- Parser Components
- Parser Installation
- Parser Testing
- Python Style Guide
- Construct Tutorial
- Style Guide
- Testing
> pip install mwcp
Alternatively you can clone this repo and install locally.
> git clone https://github.com/Defense-Cyber-Crime-Center/DC3-MWCP.git
> pip install ./DC3-MWCP
For a development mode use the -e
flag to install in editable mode:
> git clone https://github.com/Defense-Cyber-Crime-Center/DC3-MWCP.git
> pip install -e ./DC3-MWCP
DC3-MWCP optionally supports DC3-Kordesii
if it is installed. This will allow you to run any DC3-Kordesii decoder from the
mwcp.FileObject
object with the run_kordesii_decoder
function.
You can install DC3-Kordesii along with DC3-MWCP by adding [kordesii]
to your appropriate install command:
pip install mwcp[kordesii]
pip install ./DC3-MWCP[kordesii]
pip install -e ./DC3-MWCP[kordesii]
DC3-MWCP is designed to allow easy development and use of malware config parsers. DC3-MWCP is also designed to ensure that these parsers are scalable and that DC3-MWCP can be integrated in other systems.
Most automated processing systems will use a condition, such as a yara signature match, to trigger execution of an DC3-MWCP parser.
There are 3 options for integration of DC3-MWCP:
- CLI:
mwcp
- REST API:
mwcp serve
- Python API
DC3-MWCP also includes a utility for test case generation and execution.
DC3-MWCP can be used directly from the command line using the mwcp
command.
> mwcp parse foo ./README.md
----- File: README.md -----
Field Value
------------ ----------------------------------------------------------------
Parser foo
File Path README.md
Description Foo
Architecture
MD5 b21df2332fe87c0fae95bdda00b5a3c0
SHA1 8841a1fff55687ccddc587935b62667173b14bcd
SHA256 0097c13a3541a440d64155a7f4443d76597409e0f40ce3ae67f73f51f59f1930
Compile Time
Tags
---- Socket ----
Tags Address Network Protocol
------ --------- ------------------
127.0.0.1 tcp
---- URL ----
Tags Url Address Network Protocol Application Protocol
------ ---------------- --------- ------------------ ----------------------
http://127.0.0.1 127.0.0.1 tcp http
---- Residual Files ----
Tags Filename Description MD5 Arch Compile Time
------ ----------------- ------------------- -------------------------------- ------ --------------
fooconfigtest.txt example output file 5eb63bbbe01eeed093cb22bb8f5acdc3
---- Logs ----
[+] File README.md identified as Foo.
[+] size of inputfile is 15560 bytes
[+] README.md dispatched residual file: fooconfigtest.txt
[+] File fooconfigtest.txt described as example output file
[+] operating on inputfile README.md
----- File Tree -----
<README.md (b21df2332fe87c0fae95bdda00b5a3c0) : Foo>
└── <fooconfigtest.txt (5eb63bbbe01eeed093cb22bb8f5acdc3) : example output file>
see mwcp parse -h
for full set of options
DC3-MWCP can be used as a web service. The web service provides a web application as well as a REST API for some commonly used functions:
/run_parser/<parser>
-- executes a parser on uploaded file/descriptions
-- provides list of available parsers/schema.json
-- provides the schema for report output
To use, first start the server by running:
> mwcp serve
Then you can either use an HTTP client to create REST requests.
Using cURL:
> curl --form [email protected] http://localhost:8080/run_parser/foo
Using Python requests:
import requests
req = requests.post("http://localhost:8080/run_parser/foo", files={'data': open("README.md", 'rb')})
req.json()
Output:
{
"url": [
"http://127.0.0.1"
],
"address": [
"127.0.0.1"
],
"debug": [
"size of inputfile is 7128 bytes",
"outputfile: fooconfigtest.txt",
"operating on inputfile C:\\Users\\JOHN.DOE\\AppData\\Local\\Temp\\mwcp-managed_tempdir-pk0f12oh\\mwcp-inputfile-n4mw7uw3"
],
"outputfile": [
[
"fooconfigtest.txt",
"example output file",
"5eb63bbbe01eeed093cb22bb8f5acdc3",
"aGVsbG8gd29ybGQ="
]
],
"output_text": "\n----Standard Metadata----\n\nurl http://127.0.0.1\naddress 127.0.0.1\n\n----Debug----\n\nsize of inputfile
is 7128 bytes\noutputfile: fooconfigtest.txt\noperating on inputfile C:\\Users\\JOHN.DOE\\AppData\\Local\\Temp\\mwcp-managed_tempdir-pk0f12oh\\mwcp-inputfi
le-n4mw7uw3\n\n----Output Files----\n\nfooconfigtest.txt example output file\n 5eb63bbbe01eeed093cb22bb8f5acdc3\n"
}
By default, the original legacy json schema will be provided upon request.
To use the new schema, you must set the legacy
option in the query section to False
.
Eventually this new schema will replace the old one entirely. It is recommended to start using this flag to help transition your automation platform to use the new schema.
> curl --form [email protected] http://localhost:8080/run_parser/foo?legacy=False
[
{
"type": "report",
"tags": [],
"input_file": {
"type": "input_file",
"tags": [],
"name": "README.md",
"description": "Foo",
"md5": "80a3d9b88c956c960d1fea265db0882e",
"sha1": "994aa37fd26dd88272b8e661631eec8a5f425920",
"sha256": "3bef8d5dc4cd94c0ee92c9b6d7ee47a4794e550d287ee1affde84c2b7bcdf3cb",
"architecture": null,
"compile_time": null,
"file_path": "README.md",
"data": null
},
"parser": "foo",
"errors": [],
"logs": [
"[+] File README.md identified as Foo.",
"[+] size of inputfile is 15887 bytes",
"[+] README.md dispatched residual file: fooconfigtest.txt",
"[+] File fooconfigtest.txt described as example output file",
"[+] operating on inputfile README.md"
],
"metadata": [
{
"type": "url",
"tags": [],
"url": "http://127.0.0.1",
"socket": {
"type": "socket",
"tags": [],
"address": "127.0.0.1",
"port": null,
"network_protocol": "tcp",
"c2": null,
"listen": null
},
"path": null,
"query": "",
"application_protocol": "http",
"credential": null
},
{
"type": "socket",
"tags": [],
"address": "127.0.0.1",
"port": null,
"network_protocol": "tcp",
"c2": null,
"listen": null
},
{
"type": "residual_file",
"tags": [],
"name": "fooconfigtest.txt",
"description": "example output file",
"md5": "5eb63bbbe01eeed093cb22bb8f5acdc3",
"sha1": "2aae6c35c94fcfb415dbe95f408b9ce91ee846ed",
"sha256": "b94d27b9934d3e08a52e52d7da7dabfac484efe37a5380ee9088f7ace2efcde9",
"architecture": null,
"compile_time": null,
"file_path": "README.md_mwcp_output\\5eb63_fooconfigtest.txt",
"data": null
}
]
}
]
A simple HTML interface is also available at the same address. By default this
is http://localhost:8080/
. Individual samples can be submitted and results
saved as JSON, plain text, or ZIP archives.
DC3-MWCP can be run directly from Python.
#!/usr/bin/env python
"""
Simple example to demonstrate use of the API provided by DC3-MWCP framework.
"""
# first, import mwcp
import mwcp
# register the builtin MWCP parsers and any other parser packages installed on the system
mwcp.register_entry_points()
# register a directory containing parsers
mwcp.register_parser_directory(r'C:\my_parsers')
# view all available parsers
print(mwcp.get_parser_descriptions(config_only=False))
# call the run() function to to generate a mwcp.Report object.
report = mwcp.run("FooParser", "C:\\README.md")
# alternate, run on provided buffer:
report = mwcp.run("FooParser", data=b"lorem ipsum")
# Display report results in a variety of formats:
print(report.as_dict())
print(report.as_json())
print(report.as_text())
# The metadata schema has changed recently. To get the legacy format use the following:
print(report.as_dict_legacy())
print(report.as_json_legacy())
# You can also programmatically view results of report:
from mwcp import metadata
# display errors that may occur
for log in report.errors:
print(log)
# display data about original input file
print(report.input_file)
# get all url's using ftp protocol or has a query
for url in report.get(metadata.URL):
if url.application_protocol == "ftp" or url.query:
print(url.url)
# get residual files
for residual_file in report.get(metadata.File):
print(residual_file.name)
print(residual_file.description)
print(residual_file.md5)
# iterate through all metadata elements
for element in report:
print(element)
DC3-MWCP uses a configuration file which is located within the user's
profile directory. (%APPDATA%\Local\mwcp\config.yml
for Windows or ~/.config/mwcp/config.yml
for Linux)
This configuration file is used to manage configurable parameters, such as the location of the malware repository used for testing or the default parser source.
To configure this file, run mwcp config
to open up the file in your default text
editor.
An alternative configuration file can also be temporarily set using the --config
parameter.
> mwcp --config='new_config.yml' test Foo
Individual configuration parameters can be overwritten on the command line using the respective parameter.
DC3-MWCP uses Python's builtin in logging
module to log all messages.
By default, logging is configured using the log_config.yml configuration
file. Which is currently set to log all messages to the console and error messages to %LOCALAPPDATA%/mwcp/errors.log
.
You can provide your own custom log configuration file by adding the path
to the configuration parameter LOG_CONFIG_PATH
.
(Please see Python's documentation for more information on how to write your own configuration file.)
You may also use the --verbose
or --debug
flags to adjust the logging level when using the mwcp
tool.
One of the major goals of DC3-MWCP is to standardize output for malware configuration parsers, making the data from one parser comparable with that of other parsers. This is achieved by establishing a schema of standardized metadata elements that represent the common malware configuration items seen across malware families.
A formal JSON Schema can be found at schema.json, by calling mwcp schema
in the command line, or programmatically by calling mwcp.schema()
.
This schema is versioned the same as DC3-MWCP. A change in the version may not necessarily
reflect a change in the actual schema. However, any major or minor changes to the schema will
be reflected in an appropriate change to the version and will be noted in the changelog.
Please ensure you pin DC3-MWCP appropriately.
It is acknowledged that a set of generic elements will often not be adequate to capture the nuances of individual malware families. To ensure that malware family specific attributes are appropriately captured in parser output, the schema includes an "Other" element which supports arbitrary key-value pairs. The keys and values are arbitrary to permit flexibility in describing the peculiarities of individual malware families. Information not captured in the abstract standardized elements is captured through this mechanism.
The use of tags is encouraged to provide additional context for the configuration items. For example, if a specific url is used to download a second stage component, a tag of "download" could be added to the reported URL element. Alternatively, if the URL is used for a proxy, a tag of "proxy" could be included. There is no standard on what tags are available or when they should be included. This should be determined by your organization.
It is possible to extend the schema to include your own custom metadata elements.
This can be accomplished by creating a class that inherits from mwcp.metadata.Metadata
.
This class must be decorated with attr using the custom configuration mwcp.metadata.config
.
NOTE: The class name must be unique from other metadata elements.
from typing import List
import attr
import mwcp
from mwcp import metadata
@attr.s(**metadata.config)
class MyCustom(metadata.Metadata):
"""
This is my custom metadata item.
"""
field_a: str
field_b: int
field_c: List[str] = attr.ib(factory=list)
item = MyCustom(field_a="hello", field_b=42, field_c=["a", "b"])
print(item)
print(item.as_dict())
# Custom items can be included in the report like normal.
# MWCP will automatically format and display the custom element in the report.
report = mwcp.Report()
with report:
report.add(item)
print(report.as_text())
MyCustom(tags=set(), field_a='hello', field_b=42, field_c=['a', 'b'])
{'type': 'my_custom', 'tags': [], 'field_a': 'hello', 'field_b': 42, 'field_c': ['a', 'b']}
---- My Custom ----
Tags Field A Field B Field C
------ --------- --------- ----------
hello 42 a, b
Please note, that extending the schema will obviously cause the schema.json file to be incorrect.
To regenerate the schema to also include the custom element run mwcp.schema()
afterwards.
import json
import mwcp
with open("schema.json", "w") as fo:
json.dump(mwcp.schema(id="https://acme.org/0.1/schema.json"), fo, indent=4)
MWCP comes with a few helper utilities (located in mwcp.utils
) that may become useful for parsing malware files.
pefileutils
- Provides helper functions for common routines done with thepefile
library. (obtaining or checking for exports, imports, resources, sections, etc.)elffileutils
- Provides helper functions for common routines done with theelftools
library. Provides a consistent interface similar topefileutils
.custombase64
- Provides functions for base64 encoding/decoding data with a custom alphabet.construct
- Provides extended functionality to the construct library and brings back some lost features from version 2.8 into 2.9.- This library has replaced the
enstructured
library originally found in the resources directory. - Please follow this tutorial for migrating from
enstructured
toconstruct
.
- This library has replaced the
pecon
- PE file reconstruction utility.- Please see docstring in pecon.py for more information.
poshdeob
- An experimental powershell deobfuscator utility used to statically deobfuscate code and extract strings.