AIL framework is for Analysis of Information Leaks. AIL is a modular framework to analyse potential information leaks from unstructured data sources like pastes from Pastebin or similar services or unstructured data streams.
AIL framework is flexible and can be extended to support other functionalities to mine or process sensitive information (e.g. data leak prevention).
Features
Also Read – Covenant : A .NET Command & Control Framework That Aims To Highlight The Attack Surface
Type these command lines for a fully automated installation and start AIL framework:
git clone https://github.com/CIRCL/AIL-framework.git
cd AIL-framework
./installing_deps.sh
cd ~/AIL-framework/
cd bin/
./LAUNCH.sh -l
The default installing_deps.sh is for Debian and Ubuntu based distributions.
There is also a Travis file used for automating the installation that can be used to build and install AIL on other systems.
Requirement
In order to use AIL combined with ZFS or unprivileged LXC it’s necessary to disable Direct I/O in $AIL_HOME/configs/6382.conf
by changing the value of the directive use_direct_io_for_flush_and_compaction
to false
.
cd bin/
./LAUNCH -l
Eventually you can browse the status of the AIL framework website at the following URL:
https://localhost:7000/
The default credentials for the web interface are located in DEFAULT_PASSWORD
. This file is removed when you change your password.
Research Using AIL
If you write academic paper, relying or using AIL, it can be cited with the following BibTeX:
@inproceedings{mokaddem2018ail,
title={AIL-The design and implementation of an Analysis Information Leak framework},
author={Mokaddem, Sami and Wagener, G{\’e}rard and Dulaunoy, Alexandre},
booktitle={2018 IEEE International Conference on Big Data (Big Data)},
pages={5049–5057},
year={2018},
organization={IEEE}
}
Screenshots
Tor Hidden Service Crawler
Trending charts
Extracted encoded files from pastes
Browsing
Tagging system
MISP and The Hive, automatic events and alerts creation
Paste submission
Sentiment analysis
Terms manager and occurence
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