[Autogenerated] while it's important to have a solid understanding
on the core concept and how to implement them.
Also want to make you familiar with a couple of solutions for scanning files.
First one is lucky.
This is a simple scanner for indicators of compromise and it supports scanning
for foul hashes as well as yard rules and it's available on GitHub The next one
is binary alert and this one is really interesting.
It's essentially a cloud scanning solution for files but
you can deploy it in your own AWS tenant.
It's built for aWS with serverless functions so this will reduce the running
costs and you can find more information about it at binary alert dot io You
may want to develop your script into a service,
a really good course that I think you can follow on this path is
developing Python three apps with docker by Stephen Haines which
will teach you how to containerize your python application as well
as creating a web interface for it.
Also, for completeness,
I've added links related to the python modules used so far and
where you can find more details about them now.
Before moving to the next module,
I want to also take a moment and go through some of the
important topics that we've covered in this module.
We've put together a standalone Fall scanning utility that can
interact with files on disk using the os module and also accept
command line arguments using the ark parts module with the goal of
automating manual analysis.
We have generated fall and import hashes to help compare falls and also
identify common elements such as the same list of imports.
We have also leveraged ER rules to identify malicious patterns
within unknown falls and interacted with the virus Total API to
extract false reputation information.
Our journey is far from over.
And in the next module,
conducting muller analysis with python will go on a much deeper and
detailed level by focusing on malicious documents,
detecting packed executable and also investigate Python malware.