|Tools Supported||autoruns, kape, kansa, plaso, mactime, macrobber, volatility, sabonis|
|Advanced Artifact Support (HAM)||svclist, pslist, flist, amcache, evtx, winreg, fstl|
The main purpose of the project has been doing your job as easier as possible, this time creating a new module for applying Machine Learning on graphs.
|build_lm_dataset()||build_lm_dataset(options)||CLI||Build a lateral movement dataset from a log event dataset.|
|find_lm_anomalies()||find_lm_anomalies(options)||CLI||Identify anomalous lateral movements (LM) in a LM dataset.|
There are other functions available in ds4n6_lib, but we have selected the ones that are more user-friendly as the “Core” ones, which allow you to access most of the functionalities of the framework with minimum effort. In the future we will be publishing more low level details for those users who need more flexibility in order to create scripts, analysis pipelines, etc.
You can find examples on how to use core functions here.