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I have developed an IP clustering program much like the blackhat USA application shown here: https://www.blackhat.com/docs/us-16/materials/us-16-Wolff-Applied-Machine-Learning-For-Data-Exfil-And-Other-Fun-Topics.pdf

However my application has the option of using nessus exported files as well as nmap whilst concatenating the features together, this process adds the feature of vulnerabilities into the clustering algorithm.

The end result outputs graphs and statistics whilst showing the most vulnerable machines on the network. This is all done automatically in a few minutes without the user having to manually look through large files on networks with hundreds to thousands of IP's.

unfortunately I cannot provide full examples as of yet as its for my thesis, yet to be submitted.

I was wondering if this application would be useful for any network admins or pentesters, id love to give back to the community. In which case ill release it open source.

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    It is probably difficult to say how useful this would be without access to the source, your research, and results of rigorous evaluation. It sounds interesting and I hope you are able to release it as soon as you can. Even if (in the worst case scenario) others do not consider it useful, notions of usefulness are subjective and your work may have scientific merit regardless of their opinion – julian Apr 24 '17 at 21:13
  • Thanks for the info, ill try this post again once im able to actually show some of it :) – valkyrix Apr 24 '17 at 21:20
  • Depending on how your degree was funded, what is produced as part of the thesis may already be covered under institutional rules. In most cases such rules forbid you to monetarily profit from the produce without sharing this monetary profit with the university (but such rules very a lot, so your case can be different). Releasing it under a license that forbids profit from it is also a common way of dodging such clauses. That said it becomes a law and not InfoSec question, yet I'd carefully review the rules governing your degree before anything else. – grochmal Apr 25 '17 at 2:30

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