binlex

A Genetic Binary Trait Lexer Library and Utility

The purpose of binlex is to extract basic blocks and functions as traits from binaries for malware research, hunting and detection.

Most projects attempting this use Python to generate traits, but it is very slow.

The design philosophy behind binlex is it to keep it simple and extendable.

The simple command-line interface allows malware researchers and analysts to hunt traits across hundreds or thousands of potentially similar malware saving time and money in production environments.

While the C++ API allows developers to get creative with their own detection solutions, completely unencumbered by license limitations.

To help combat malware, we firmly commit our work to the public domain for the greater good of the world.

Demos

Get slides here.

Use Cases

YARA Signature Creation/Automation
Identifying Code-Reuse
Threat Hunting
Building Goodware Trait Corpus
Building Malware Trait Corpus
Genetic Programming
Machine Learning Malware Detection

Installation

Dependencies:

To get started you will need the following dependencies for binlex.

sudo apt install -y git build-essential \
                    cmake make parallel \
                    doxygen git-lfs rpm \
                    python3 python3-dev
git clone --recursive https://github.com/c3rb3ru5d3d53c/binlex.git
cd binlex/

NOTE: that binlex requires cmake >= 3.5, make >= 4.2.1 and ubuntu >= 20.04.

Once you have installed, cloned and changed your directory to the project directory, we can continue with installation.

From Source:

If you want to compile and install via make install run the following commands:

make threads=4
sudo make install

# Test your Install
binlex -m auto -i tests/elf/elf.x86

Binary Releases:

See the releases page.

If you need the bleeding edge binaries you can download them from our AppVeyor CI/CD here.

NOTE: bleeding edge binaries are subject to bugs, if you encounter one, please let us know!

Test Files:

To download all the test samples do the command git lfs fetch
ZIP files in the tests/ directory can then be extracted using the password infected

NOTE: The tests/ directory contains malware, we assume you know what you are doing.

To download individual git-lfs files from a relative path, you can use the following git alias in ~/.gitconfig:

[alias]
download = "!ROOT=$(git rev-parse --show-toplevel); cd $ROOT; git lfs pull --include $GIT_PREFIX$1; cd $ROOT/$GIT_PREFIX"

You will then be able to do the following:

git download tests/pe/pe.zip

Building Packages:

Additionally, another option is to build Debian binary packages for and install those.

To build packages use cpack, which comes with cmake.

make threads=4
make pkg  # builds binary packages
make dist # builds source packages
sudo apt install ./build/binlex_1.1.1_amd64.deb
binlex -m elf:x86 -i tests/elf/elf.x86

You will then be provided with .deb, .rpm and .tar.gz packages for binlex.

Building Python Bindings:

To get started using pybinlex:

virtualenv -p python3 venv
source venv/bin/activate
# Install Library
pip install -v .
# Build Wheel Package
pip wheel -v -w build/ .
python3
>>> import pybinlex

If you wish to compile the bindings with cmake:

make python

NOTE: we use pybind11 and support for python3.9 is experimental.

Examples of how to use pybinlex can be found in tests/tests.py.

Building Binlex Platform:

Install Dependencies:

sudo apt install docker.io make
sudo usermod -a -G docker $USER
sudo systemctl enable docker
reboot # ensures your user is added to the docker group

Building Containers:

make docker        # generate docker-compose.yml and config files
# Your generated credentials will be printed to the screen and saved in config/credentials.txt
make docker-build  # build the images (can take a long time, go get a coffee!)
make docker-start  # start the containers
make docker-init   # initialize all databases and generated configurations
make docker-logs   # tail all logs

If you wish to change the auto-generated initial username and passwords, you can run ./docker.sh with additional parameters.

To see what parameters are available to you, run ./docker.sh --help.

Platform URLs:

HTTP API https://127.0.0.1:8443 (API Docs)
RabbitMQ https://127.0.0.1:15672/ (Messaging)
MinIO https://127.0.0.1:9001/ (Object Store)
MongoDB mongodb://<user>:<pass>@127.0.0.1 (MongoDB)
User mongodb://binlex:<generated-password>@127.0.0.1/?authSource=binlex (for trait collection)
Admin mongodb://admin:<generated-password>@127.0.0.1 (for administration)

Example HTTP API Requests:

# Get Version
curl --insecure -H "X-API-Key: <key>" https://127.0.0.1:8443/binlex/api/v1/version

# Upload Sample
curl -X POST --insecure -H "X-API-Key: <key>" --upload-file <file> https://127.0.0.1:8443/binlex/api/v1/samples/<corpus>/<mode>

# List Corpra
curl -X GET --insecure -H "X-API-Key: <key>" https://127.0.0.1:8443/binlex/api/v1/corpra

# Get Supported Modes
curl -X GET --insecure -H "X-API-Key: <key>" https://127.0.0.1:8443/binlex/api/v1/modes

# Download Sample by SHA256
curl -X GET --insecure -H "X-API-Key: <key>" https://127.0.0.1:8443/binlex/api/v1/samples/<sha256>

If you work with a team of malware analysts or malware researchers, you create read-only accounts for them.

This will ensure they can do advanced queries to hunt and write detection signatures.

Adding New Read-Only Users to MongoDB:

cd scripts/
./mongodb-createuser.sh mongodb-router1 <username> <password>

If you have a VERY large team, you can script creation of these accounts.

CLI Usage

binlex v1.1.1 - A Binary Genetic Traits Lexer
  -i  --input           input file              (required)
  -m  --mode            set mode                (optional)
  -lm --list-modes      list modes              (optional)
      --instructions    include insn traits     (optional)
  -c  --corpus          corpus name             (optional)
  -g  --tag             add a tag               (optional)
                        (can be specified multiple times)
  -t  --threads         number of threads       (optional)
  -to --timeout         execution timeout in s  (optional)
  -h  --help            display help            (optional)
  -o  --output          output file             (optional)
  -p  --pretty          pretty output           (optional)
  -d  --debug           print debug info        (optional)
  -v  --version         display version         (optional)
Author: @c3rb3ru5d3d53c

Supported Modes

elf:x86
elf:x86_64
pe:x86
pe:x86_64
pe:cil
raw:x86
raw:x86_64
raw:cil
auto

NOTE: The raw modes can be used on shellcode.

NOTE: The auto mode cannot be used on shellcode.

Advanced

If you are hunting using binlex you can use jq to your advantage for advanced searches.

build/binlex -m auto -i tests/pe/pe.x86 | jq -r 'select((.size > 8 and .size < 16) and (.bytes_sha256 != .traits.sha256)) | .trait' | head -10
8b 48 ?? 03 c8 81 39 50 45 00 00 75 12
0f b7 41 ?? 3d 0b 01 00 00 74 1f
83 b9 ?? ?? ?? ?? ?? 76 f2
33 c0 39 b9 ?? ?? ?? ?? eb 0e
83 4d ?? ?? b8 ff 00 00 00 e9 ba 00 00 00
89 75 ?? 66 83 3e 22 75 45
03 f3 89 75 ?? 66 8b 06 66 3b c7 74 06
03 f3 89 75 ?? 66 8b 06 66 3b c7 74 06
56 ff 15 ?? ?? ?? ?? ff 15 ?? ?? ?? ?? eb 2d
55 8b ec 51 56 33 f6 66 89 33 8a 07 eb 29

Here are examples of additional queries.

# Block traits with a size between 0 and 32 bytes
jq -r 'select(.type == "block" and .size < 32 and .size > 0)'
# Function traits with a cyclomatic complexity greater than 32 (maybe obfuscation)
jq -r 'select(.type == "function" and .cyclomatic_complexity > 32)'
# Traits where bytes have high entropy
jq -r 'select(.bytes_entropy > 7)'
# Output all trait strings only
jq -r '.trait'
# Output only trait hashes
jq -r '.trait_sha256'

If you output just traits you want to stdout you can do build a yara signature on the fly with the included tool blyara:

build/binlex -m raw:x86 -i tests/raw/raw.x86 | jq -r 'select(.size > 16 and .size < 32) | .trait' | build/blyara --name example_0 -m author example -m tlp white -c 1
rule example_0 {
    metadata:
        author = "example"
        tlp = "white"
    strings:
        trait_0 = {52 57 8b 52 ?? 8b 42 ?? 01 d0 8b 40 ?? 85 c0 74 4c}
        trait_1 = {49 8b 34 8b 01 d6 31 ff 31 c0 c1 cf ?? ac 01 c7 38 e0 75 f4}
        trait_2 = {e8 67 00 00 00 6a 00 6a ?? 56 57 68 ?? ?? ?? ?? ff d5 83 f8 00 7e 36}
    condition:
        1 of them
}

You can also use the switch --pretty to output json to identify more properies to query.

build/binlex -m auto -i tests/pe/pe.emotet.x86 -c malware -g malware:emotet -g malware:loader | head -1 | jq
{
  "average_instructions_per_block": 29,
  "blocks": 1,
  "bytes": "55 8b ec 83 ec 1c 83 65 f0 00 33 d2 c7 45 e4 68 5d df 00 c7 45 e8 43 c4 cb 00 c7 45 ec 8f 08 46 00 c7 45 f8 06 3b 43 00 81 45 f8 25 7a ff ff 81 75 f8 30 f4 44 00 c7 45 fc 22 51 53 00 8b 45 fc 6a 3f 59 f7 f1 6a 1c 89 45 fc 33 d2 8b 45 fc 59 f7 f1 89 45 fc 81 75 fc 3c 95 0e 00 c7 45 f4 0b 16 11 00 81 45 f4 e1 21 ff ff 81 75 f4 79 bd 15 00 ff 4d 0c 75 21",
  "bytes_entropy": 5.333979606628418,
  "bytes_sha256": "13e0463c5837bc5ce110990d69397662b82b8de8a9971f77b237f2a6dd2d8982",
  "corpus": "malware",
  "cyclomatic_complexity": 3,
  "edges": 2,
  "file_sha256": "7b01c7c835552b17f17ad85b8f900c006dd8811d708781b5f49f231448aaccd3",
  "file_tlsh": "42E34A10F3D341F7DC9608F219B6B22F9F791E023124DFA987981F57ADB5246A2B981C",
  "instructions": 29,
  "invalid_instructions": 0,
  "mode": "pe:x86",
  "offset": 49711,
  "size": 118,
  "tags": [
    "malware:emotet",
    "malware:loader"
  ],
  "trait": "55 8b ec 83 ec 1c 83 65 ?? ?? 33 d2 c7 45 ?? ?? ?? ?? ?? c7 45 ?? ?? ?? ?? ?? c7 45 ?? ?? ?? ?? ?? c7 45 ?? ?? ?? ?? ?? 81 45 ?? ?? ?? ?? ?? 81 75 ?? ?? ?? ?? ?? c7 45 ?? ?? ?? ?? ?? 8b 45 ?? 6a 3f 59 f7 f1 6a 1c 89 45 ?? 33 d2 8b 45 ?? 59 f7 f1 89 45 ?? 81 75 ?? ?? ?? ?? ?? c7 45 ?? ?? ?? ?? ?? 81 45 ?? ?? ?? ?? ?? 81 75 ?? ?? ?? ?? ?? ff 4d ?? 75 21",
  "trait_entropy": 3.9699645042419434,
  "trait_sha256": "7b04c2dbcc3cf23abfdd457b592b4517e4d98b5c83e692c836cde5b91899dd68",
  "type": "block"
}

If you have terabytes of executable files, we can leverage the power of parallel to generate traits for us.

make traits source=samples/malware/pe/x32/ dest=dist/ type=malware format=pe arch=x86 threads=4
make traits-combine source=dist/ dest=dist/ type=malware format=pe arch=x86 threads=4

It also allows you to name your type of dataset, i.e. goodware/malware/riskware/pua etc...

With binlex it is up to you to remove goodware traits from your extracted traits.

There have been many questions about removing "library code", there is a make target shown below to help you with this.

make traits-clean remove=goodware.traits source=sample.traits dest=malware.traits

With binlex the power is in your hands, "With great power comes great responsibility", it is up to you!

Plugins:

There has been some interest in making IDA, Ghidra and Cutter plugins for binlex.

This is something that will be started soon as we finish the HTTP API endpoints.

This README.md will be updated when they are ready to use.

General Usage Information:

Binlex is designed to do one thing and one thing only, extract genetic traits from executable code in files. This means it is up to you "the researcher" / "the data scientist" to determine which traits are good and which traits are bad. To accomplish this, you need to use your own fitness function. I encourage you to read about genetic programming to gain a better understanding of this in practice. Perhaps watching this introductory video will help your understanding.

Again, it's up to you to implement your own algorithms for detection based on the genetic traits you extract.

Trait Format

Traits will contain binary code represented in hexadecimal form and will use ?? as wild cards for memory operands or other operands subject to change.

They will also contain additional properties about the trait including its offset, edges, blocks, cyclomatic_complexity, average_instruction_per_block, bytes, trait, trait_sha256, bytes_sha256, trait_entropy, bytes_entropy, type, size, invalid_instructions and instructions.

{
  "average_instructions_per_block": 6,
  "blocks": 1,
  "bytes": "8b 45 08 a3 10 52 02 10 8b 45 f8 e8 fb d7 00 00 85 c0 74 0d",
  "bytes_entropy": 3.9219279289245605,
  "bytes_sha256": "435cb166701006282e457d441ca793e795e38790cacc5b250d4bc418a28961c3",
  "corpus": "malware",
  "cyclomatic_complexity": 3,
  "edges": 2,
  "file_sha256": "7b01c7c835552b17f17ad85b8f900c006dd8811d708781b5f49f231448aaccd3",
  "file_tlsh": "42E34A10F3D341F7DC9608F219B6B22F9F791E023124DFA987981F57ADB5246A2B981C",
  "instructions": 6,
  "invalid_instructions": 0,
  "mode": "pe:x86",
  "offset": 49829,
  "size": 20,
  "tags": [
    "malware:emotet",
    "malware:loader"
  ],
  "trait": "8b 45 ?? a3 ?? ?? ?? ?? 8b 45 ?? e8 fb d7 00 00 85 c0 74 0d",
  "trait_entropy": 3.3787841796875,
  "trait_sha256": "fe3b057a28b40a02ac9dd2db6c3208f96f7151fb912fb3c562a7b4581bb7f7a0",
  "type": "block"
}

Documentation

Public documentation on binlex can be viewed here.

Building Docs

You can access the C++ API Documentation and everything else by building the documents using doxygen.

make docs threads=4

The documents will be available at build/docs/html/index.html.

C++ API Example Code

The power of detection is in your hands, binlex is a framework, leverage the C++ API.

#include <binlex/pe.h>
#include <binlex/decompiler.h>

using namespace binlex;

int main(int argc, char **argv){
  PE pe32;
  if (pe32.ReadFile(argv[1]) == false){
      return EXIT_FAILURE;
  }
  Decompiler decompiler(pe32);
  decompiler.Decompile();
  decompiler.WriteTraits();
  return EXIT_SUCCESS;
}

Python API Example Code

The power of detection is in your hands, binlex is a framework, leverage the C++ API.

#!/usr/bin/env python

import pybinlex

pe = pybinlex.PE()
result = pe.read_file('example.exe')
if result is False: sys.exit(1)
decompiler = pybinlex.Decompiler(pe)
decompiler.decompile()
traits = decompiler.get_traits()
print(json.dumps(traits, indent=4))

We hope this encourages people to build their own detection solutions based on binary genetic traits.

Tips

If you are hunting be sure to use jq to improve your searches
Does not support PE files that are VB6 if you run against these you will get errors
Don't mix packed and unpacked malware or you will taint your dataset (seen this in academics all the time)
When comparing samples to identify if their code is similar, DO NOT mix their architectures in your comparison
- Comparing CIL byte-code or .NET to x86 machine code may yield interesting properties, but they are invalid
Verify the samples you are collecting into a group using skilled analysts
These traits are best used with a hybrid approach (supervised)

Example Fitness Model

Traits will be compared amongst their common malware family, any traits not common to all samples will be discarded.

Once completed, all remaining traits will be compared to traits from a goodware set, any traits that match the goodware set will be discarded.

To further differ the traits from other malware families, the remaining population will be compared to other malware families, any that match will be discarded.

The remaining population of traits will be unique to the malware family tested and not legitimate binaries or other malware families.

This fitness model allows for accurate classification of the tested malware family.

Future Work

Java byte-code Support raw:jvm, java:jvm
Python byte-code Support raw:pyc, python:pyc
More API Endpoints
Cutter, Ghidra and IDA Plugins
Mac-O Support macho:x86_64, macho:x86

Contributing

If you wish to contribute to Binlex DM me on Twitter here.

You can also join our Discord here.

Currently looking for help on:

MacOS Developer (Parse Mach-O)
Plugin Developers (Python)
Front-End Developers (Python)

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README.md

README.md

binlex

A Genetic Binary Trait Lexer Library and Utility

Demos

Use Cases

Installation

Building Binlex Platform:

CLI Usage

Trait Format

Documentation

Building Docs

C++ API Example Code

Python API Example Code

Tips

Example Fitness Model

Future Work

Contributing

Files

README.md

Latest commit

History

README.md

File metadata and controls

binlex

A Genetic Binary Trait Lexer Library and Utility

Demos

Use Cases

Installation

Building Binlex Platform:

CLI Usage

Trait Format

Documentation

Building Docs

C++ API Example Code

Python API Example Code

Tips

Example Fitness Model

Future Work

Contributing