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Sandbox for automated Linux malware analysis.
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README.md

LiSa

Project providing automated Linux malware analysis on various CPU architectures.

Table of contents

LiSa

Features

  • QEMU emulation.
  • Currently supporting x86_64, i386, arm, mips, aarch64.
  • Small images built w/ buildroot.
  • Radare2 based static analysis.
  • Dynamic (behavioral) analysis using SystemTap kernel modules - captured syscalls, openfiles, process trees.
  • Network statistics and analysis of DNS, HTTP, Telnet and IRC communication.
  • Endpoints analysis and blacklists configuration.
  • Scaled with celery and RabbitMQ.
  • REST API | frontend.
  • Extensible through sub-analysis modules and custom images.

Get Started

Requirements

  1. Get repository.
$ git clone https://github.com/danieluhricek/lisa
$ cd lisa
  1. Build.
# docker-compose build
  1. Run the sandbox (default location: http://localhost:4242).
# docker-compose up

Configuration

Web hosting

Setup your server's IP:port in nginx service in docker-compose.yml.

.
.
  nginx:
    image: lisa-nginx
    build:
      context: .
      dockerfile: ./docker/nginx/Dockerfile
      args:
        webhost: <myip|default=localhost>:<port>
    ports:
      - <port>:80
.
.

Scaling

Workers are scalable.

# docker-compose up --scale worker=10

VPN

You can route malware's traffic through OpenVPN. In order to do that:

  1. Mount volume containing OpenVPN config (named config.ovpn).
  2. Set environment valirable VPN to OpenVPN config's directory path.
.
.
  worker:
    image: lisa-worker
    build:
      context: .
      dockerfile: ./docker/worker/Dockerfile
    environment:
      - VPN=/vpn
    volumes:
      - "./data/storage:/home/lisa/data/storage"
      - "./vpn:/vpn"
.
.

Blacklists

Default used blacklists are (source):

  • bi_ssh_2_30d.ipset
  • firehol_level3.netset
  • firehol_webserver.netset
  • iblocklist_abuse_zeus.netset
  • normshield_all_wannacry.ipset

If you want to use any other blacklist, put .ipset or .netset files into data/blacklists. All of these blacklists are merged during build of worker service.

Adding new sub-analysis modules

Core of LiSa project supports 4 basic modules of analysis: static_analysis, dynamic_analysis, network_analysis and virustotal. Sub-analysis modules are plugin-based. For adding new sub-analysis and appending it's output to final json do following:

  1. Create class which inherits from AbstractSubAnalyzer class and implement run_analysis() method eg.:
class NewSubAnalyzer(AbstractSubAnalyzer):
    def run_analysis(self):
        pass
  1. Update list in lisa.config.py :
analyzers_config = [
    # core analyzers
    'lisa.analysis.static_analysis.StaticAnalyzer',
    'lisa.analysis.dynamic_analysis.DynamicAnalyzer',
    'lisa.analysis.network_analysis.NetworkAnalyzer',
    'lisa.analysis.virustotal.VirusTotalAnalyzer',

    # custom
    'module_of_new_analyzer.NewSubAnalyzer'
]

Running tests

# docker build -f ./docker/tests/Dockerfile -t lisa-tests .
# docker run lisa-tests

Upcoming features

  1. YARA module - YARA module to match patterns in LiSa's JSON output.
  2. Images selection - More Linux images containing e.g. IoT firmware.

Contribute

Contributions | feedback | issues | pull requests are welcome.

License

LiSa is licensed under Apache License 2.0.

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