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 Unsupervised ML Intrusion detection system
#1
Hey guys, I am new to coding. I study Digital Forensics, security and ethical hacking at uni in which I want to pursue Pen testing.
For my 4th year dissertation I am trying to make an Intrusion detection system that utilises unsupervised machine learning, k-means clustering. I have an industry standard dataset made up of begnin and network attack pcap's, fed into jupyter notebook, which I want to be clustered into Begnin traffic or attacks.

Github code: https://github.com/CammyS32/Project (PractiseHonsProject)
Datasheet: https://www.dropbox.com/s/kn3w77zgnjhxty...v.csv?dl=0

So far I have some code working however I don't understand the output of my code. The scatter plot doesn't look like other k-means scatter plots and I can't visualise how I am going to evaluate the output. I am hoping you can shed some light on the output of my code and some next steps. Any tips on how to improve my code are welcomed too Razz
Tia,
Cammy
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