Skip to main content

No project description provided

Project description

ARTSEM: Anti-Reversing Trace Scanner for ELF Malware

PyPI - Version PyPI - Python Version


Note: Although "Malware" is included in the name the tool can be used on any purpose Linux executables.

Table of Contents

  1. Description
  2. Installation
  3. The dataset
  4. Roadmap
  5. License

Description

This project aims to create an automated tool able to detect which anti-analysis techniques had been applied to a binary.

First we will analyze some techniques (anti-debugging, anti-disassembly, etc.) and the differences in the binaries when they are used.

Then, we will look for traces, patterns and other evidences that allow us to detect the usage of anti-analysis features.

Finally, we will use the tool with a real ELF malware dataset, to see which and how often these techniques are used in the wild.

Installation

pip install artsem

The dataset

The malware samples conforming the dataset have been obtained from different sources. Thanks to you all.


Roadmap

Milestone 1

Generate a (test) dataset from known sources (e.g. 'ls'). To do so, compile the selected program with different flags and analyze the differences between all the binaries generated

Milestone 2

Create a script able to detect the usage of different anti-analysis techniques. It will run different tests on compiled binaries looking for possible traces left by the usage of these techniques

Milestone 3

Use the script with the malware dataset

Milestone 4

Analyze the results. Which techniques were easier to spot? Which ones were more difficult? Are there false positives?

License

artsem is distributed under the terms of the MIT license.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

artsem-0.0.43.tar.gz (66.2 kB view details)

Uploaded Source

Built Distribution

artsem-0.0.43-py3-none-any.whl (4.8 kB view details)

Uploaded Python 3

File details

Details for the file artsem-0.0.43.tar.gz.

File metadata

  • Download URL: artsem-0.0.43.tar.gz
  • Upload date:
  • Size: 66.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-httpx/0.27.0

File hashes

Hashes for artsem-0.0.43.tar.gz
Algorithm Hash digest
SHA256 ebf128c8836418b2070cca41243b663e9ca6a9b99dba333c79376b480ebdbb45
MD5 96fbe2202a2604e5d97e116660fd1d1c
BLAKE2b-256 f1d129af2c1a2252f2d44104142b4a6b208c042672d134d76743e06b847e0a85

See more details on using hashes here.

File details

Details for the file artsem-0.0.43-py3-none-any.whl.

File metadata

  • Download URL: artsem-0.0.43-py3-none-any.whl
  • Upload date:
  • Size: 4.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-httpx/0.27.0

File hashes

Hashes for artsem-0.0.43-py3-none-any.whl
Algorithm Hash digest
SHA256 e4f41b2e58dc90c469e10ab161e6e71d2010e76935309133081f0661178c1951
MD5 168a94f9cd113822e52b02ff2c28268b
BLAKE2b-256 7bf3afaa6ad920726846e942109b3e3046e3675e6a14ed789fe2b579bfaf6af3

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page