recommenders VS Metasploit

recommenders – Best Practices on Recommendation Systems

I would like to do a benchmarking on the Microsoft models SVD, SAR and NMF (available here: https://github.com/microsoft/recommenders) but with this input data I get a precision and recall close to zero. Any ideas how I can improve this? For SVD and NMF (surprise library) the model wants a rating input that is normally distributed, which it not the case for my binary data where the transactions all have a rating of 1.

Then I tried to find some more advanced models and I found this really good list and in there I found the Microsoft one. So it's' where we are now, which a bunch of different models and not a documentation/tutorials out there.

Metasploit

Metasploit: https://github.com/rapid7/metasploit-framework

1-) Learn Hacking on a debian based distro like Kali Linux - I personally started with tools like nikto, camhacker... and then moved to more complex frameworks like metasploit.

I once had to give a presentation about Metasploit, and whether it was ethically correct for the creator to make it free and open-source, available to everyone. And in researching this I realized that there were a lot of parallels between the arguments for or against hacking tools being readily available and the arguments for or against gun control. I'll just list a few quickly:

Metasploit Framework (mentioned earlier)

This phase is where the pen testers practically prove that there exist potential vulnerabilities in the target system. The pen testers do the hacking using an array of technical approaches and social engineering methods to exploit the vulnerabilities. The ethical hackers commonly use Metasploit framework to automatically execute exploitation against the target systems. Moreover, they may install malwares such as rootkit to persistently maintain their foothold and further compromise the target system.