Various capabilities for static malware analysis.

Overview

Malchive

The malchive serves as a compendium for a variety of capabilities mainly pertaining to malware analysis, such as scripts supporting day to day binary analysis and decoder modules for various components of malicious code.

The goals behind the 'malchive' are to:

  • Allow teams to centralize efforts made in this realm and enforce communication and continuity
  • Have a shared corpus of tools for people to build on
  • Enforce clean coding practices
  • Allow others to interface with project members to develop their own capabilities
  • Promote a positive feedback loop between Threat Intel and Reverse Engineering staff
  • Make static file analysis more accessible
  • Serve as a vehicle to communicate the unique opportunity space identified via deep dive analysis

Documentation

At its core, malchive is a bunch of standalone scripts organized in a manner that the authors hope promotes the project's goals.

To view the documentation associated with this project, checkout the wiki page!

Scripts within the malchive are split up into the following core categories:

  • Utilities - These scripts may be run standalone to assist with static binary analysis or as modules supporting a broader program. Utilities always have a standalone component.
  • Helpers - These modules primarily serve to assist components in one or more of the other categories. They generally do not have a stand-alone component and instead serve the intents of those that do.
  • Binary Decoders - The purpose of scripts in this category is to retrieve, decrypt, and return embedded data (typically inside malware).
  • Active Discovery - Standalone scripts designed to emulate a small portion of a malware family's protocol for the purposes of discovering active controllers.

Installation

The malchive is a packaged distribution that is easily installed and will automatically create console stand-alone scripts.

Steps

You will need to install some dependencies for some of the required Python modules to function correctly.

  • First do a source install of YARA and make sure you compile using --dotnet
  • Next source install the YARA Python package.
  • Ensure you have sqlite3-dev installed
    • Debian: libsqlite3-dev
    • Red Hat: sqlite-devel / pip install pysqlite3

You can then clone the malchive repo and install...

  • pip install . when in the parent directory.
  • To remove, just pip uninstall malchive

Scripts

Console scripts stemming from utilities are appended with the prefix malutil, decoders are appended with maldec, and active discovery scripts are appended with maldisc. This allows for easily identifiable malchive scripts via tab autocompletion.

; running superstrings from cmd line
malutil-superstrings 1.exe -ss
0x9535 (stack) lstrlenA
0x9592 (stack) GetFileSize
0x95dd (stack) WriteFile
0x963e (stack) CreateFileA
0x96b0 (stack) SetFilePointer
0x9707 (stack) GetSystemDirectoryA

; running a decoder from cmd line
maldec-pivy test.exe_
{
    "MD5": "2973ee05b13a575c06d23891ab83e067",
    "Config": {
        "PersistActiveSetupName": "StubPath",
        "DefaultBrowserKey": "SOFTWARE\\Classes\\http\\shell\\open\\command",
        "PersistActiveSetupKeyPart": "Software\\Microsoft\\Active Setup\\Installed Components\\",
        "ServerId": "TEST - WIN_XP",
        "Callbacks": [
            {
                "callback": "192.168.1.104",
                "protocol": "Direct",
                "port": 3333
            },
            {
                "callback": "192.168.1.111",
                "protocol": "Direct",
                "port": 4444
            }
        ],
        "ProxyCfgPresent": false,
        "Password": "test$321$",
        "Mutex": ")#V0qA.I4",
        "CopyAsADS": true,
        "Melt": true,
        "InjectPersist": true,
        "Inject": true
    }
}

; cmd line use with other common utilities
echo -ne 'eJw9kLFuwzAMRIEC7ZylrVGgRSFZiUbBZmwqsMUP0VfcnuQn+rMde7KLTBIPj0ce34tHyMUJjrnw
p3apz1kicjoJrDRlQihwOXmpL4RmSR5qhEU9MqvgWo8XqGMLJd+sKNQPK0dIGjK+e5WANIT6NeOs
k2mI5NmYAmcrkbn4oLPK5gZX+hVlRoKloMV20uQknv2EPunHKQtcig1cpHY4Jodie5pRViV+rp1t
629J6Dyu4hwLR97LINqY5rYILm1hhlvinoyJZavOKTrwBHTwpZ9yPSzidUiPt8PUTkZ0FBfayWLp
a71e8U8YDrbtu0aWDj+/eBOu+jRkYabX+3hPu9LZ5fb41T+7fmRf' | base64 -d | zlib-flate -uncompress | malutil-xor - [KEY]

Interfacing

Utilities, decoders, and discovery scripts in this collection are designed to support single ad-hoc analysis as well as inclusion into other frameworks. After installation, the malchive should be part of your Python path. At this point accessing any of the scripts is straight forward.

Here are a few examples:

; accessing decoder modules
import sys
from malchive.decoders import testdecoder

p = testdecoder.GetConfig(open(sys.argv[1], 'rb').read())
print('password', p.rc4_key)
for c in p.callbacks:
    print('c2 address', c)

; accessing utilities
from malchive.utilities import xor
ret = xor.GenericXor(buff=b'testing', key=[0x51], count=0xff)
print(ret.run_crypt())

; accessing helpers
from malchive.helpers import winfunc
key = winfunc.CryptDeriveKey(b'testdatatestdata')

To understand more about a given module, see the associated wiki entry.

Contributing

Contributing to the malchive is easy, just ensure the following requirements are met:

  • When writing utilities, decoders, or discovery scripts, consider using the available templates or review existing code if you're not sure how to get started.
  • Make sure modification or contributions pass pre-commit tests.
  • Ensure the contribution is placed in one of the component folders.
  • Updated the setup file if needed with an entry.
  • Python3 is a must.

Legal

©2021 The MITRE Corporation. ALL RIGHTS RESERVED.

Approved for Public Release; Distribution Unlimited. Public Release Case Number 21-0153

Owner
MITRE Cybersecurity
MITRE Cybersecurity
APEACH: Attacking Pejorative Expressions with Analysis on Crowd-generated Hate Speech Evaluation Datasets

APEACH - Korean Hate Speech Evaluation Datasets APEACH is the first crowd-generated Korean evaluation dataset for hate speech detection. Sentences of

Kevin-Yang 70 Dec 06, 2022
Unofficial Implementation of Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration

Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration This repo contains only model Implementation of Zero-Shot Text-to-Speech for Text

Rishikesh (ऋषिकेश) 33 Sep 22, 2022
iSTFTNet : Fast and Lightweight Mel-spectrogram Vocoder Incorporating Inverse Short-time Fourier Transform

iSTFTNet : Fast and Lightweight Mel-spectrogram Vocoder Incorporating Inverse Short-time Fourier Transform This repo try to implement iSTFTNet : Fast

Rishikesh (ऋषिकेश) 126 Jan 02, 2023
A collection of scripts to preprocess ASR datasets and finetune language-specific Wav2Vec2 XLSR models

wav2vec-toolkit A collection of scripts to preprocess ASR datasets and finetune language-specific Wav2Vec2 XLSR models This repository accompanies the

Anton Lozhkov 29 Oct 23, 2022
2021海华AI挑战赛·中文阅读理解·技术组·第三名

文字是人类用以记录和表达的最基本工具,也是信息传播的重要媒介。透过文字与符号,我们可以追寻人类文明的起源,可以传播知识与经验,读懂文字是认识与了解的第一步。对于人工智能而言,它的核心问题之一就是认知,而认知的核心则是语义理解。

21 Dec 26, 2022
The proliferation of disinformation across social media has led the application of deep learning techniques to detect fake news.

Fake News Detection Overview The proliferation of disinformation across social media has led the application of deep learning techniques to detect fak

Kushal Shingote 1 Feb 08, 2022
Implementation of paper Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa.

RoBERTaABSA This repo contains the code for NAACL 2021 paper titled Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoB

106 Nov 28, 2022
novel deep learning research works with PaddlePaddle

Research 发布基于飞桨的前沿研究工作,包括CV、NLP、KG、STDM等领域的顶会论文和比赛冠军模型。 目录 计算机视觉(Computer Vision) 自然语言处理(Natrual Language Processing) 知识图谱(Knowledge Graph) 时空数据挖掘(Spa

1.5k Jan 03, 2023
CCF BDCI BERT系统调优赛题baseline(Pytorch版本)

CCF BDCI BERT系统调优赛题baseline(Pytorch版本) 此版本基于Pytorch后端的huggingface进行实现。由于此实现使用了Oneflow的dataloader作为数据读入的方式,因此也需要安装Oneflow。其它框架的数据读取可以参考OneflowDataloade

Ziqi Zhou 9 Oct 13, 2022
Deep Learning Topics with Computer Vision & NLP

Deep learning Udacity Course Deep Learning Topics with Computer Vision & NLP for the AWS Machine Learning Engineer Nanodegree Program Tasks are mostly

Simona Mircheva 1 Jan 20, 2022
Integrating the Best of TF into PyTorch, for Machine Learning, Natural Language Processing, and Text Generation. This is part of the CASL project: http://casl-project.ai/

Texar-PyTorch is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar

ASYML 726 Dec 30, 2022
BERN2: an advanced neural biomedical namedentity recognition and normalization tool

BERN2 We present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by

DMIS Laboratory - Korea University 99 Jan 06, 2023
Pretrain CPM - 大规模预训练语言模型的预训练代码

CPM-Pretrain 版本更新记录 为了促进中文自然语言处理研究的发展,本项目提供了大规模预训练语言模型的预训练代码。项目主要基于DeepSpeed、Megatron实现,可以支持数据并行、模型加速、流水并行的代码。 安装 1、首先安装pytorch等基础依赖,再安装APEX以支持fp16。 p

Tsinghua AI 37 Dec 06, 2022
NLTK Source

Natural Language Toolkit (NLTK) NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting

Natural Language Toolkit 11.4k Jan 04, 2023
使用Mask LM预训练任务来预训练Bert模型。训练垂直领域语料的模型表征,提升下游任务的表现。

Pretrain_Bert_with_MaskLM Info 使用Mask LM预训练任务来预训练Bert模型。 基于pytorch框架,训练关于垂直领域语料的预训练语言模型,目的是提升下游任务的表现。 Pretraining Task Mask Language Model,简称Mask LM,即

Desmond Ng 24 Dec 10, 2022
Twewy-discord-chatbot - Build a Discord AI Chatbot that Speaks like Your Favorite Character

Build a Discord AI Chatbot that Speaks like Your Favorite Character! This is a Discord AI Chatbot that uses the Microsoft DialoGPT conversational mode

Lynn Zheng 231 Dec 30, 2022
In this repository we have tested 3 VQA models on the ImageCLEF-2019 dataset.

Med-VQA In this repository we have tested 3 VQA models on the ImageCLEF-2019 dataset. Two of these are made on top of Facebook AI Reasearch's Multi-Mo

Kshitij Ambilduke 8 Apr 14, 2022
This repo stores the codes for topic modeling on palliative care journals.

This repo stores the codes for topic modeling on palliative care journals. Data Preparation You first need to download the journal papers. bash 1_down

3 Dec 20, 2022
Utilizing RBERT model for KLUE Relation Extraction task

RBERT for Relation Extraction task for KLUE Project Description Relation Extraction task is one of the task of Korean Language Understanding Evaluatio

snoop2head 14 Nov 15, 2022
A flask application to predict the speech emotion of any .wav file.

This is a speech emotion recognition app. It will allow you to train a modular MLP model with the RAVDESS dataset, and then use that model with a flask application to predict the speech emotion of an

Aryan Vijaywargia 2 Dec 15, 2021