Visualizing Malicious Clusters & Outliers

So, I’ve had some quick, consecutive blog posts around this R package I’m working on, and this one is more of an answer to my own, self-identified question of “so what?”. As I was working on an importer for AlienValut’s IP reputation database, I thought it might be interesting to visualize aspects of that data using some of the meta-information gained from the other “netintel” (my working name for the package) functions.

Acting on that impulse, I extracted all IPs that were uniquely identified as “Malicious Host“s (it’s a category in their database), did ASN & peer lookups for them and made two DrL graphs from them (I did a test singular graph but it would require a Times Square monitor to view).



You’ll need to select both images to make them bigger to view them more easily. Red nodes are hosts, blue ones are the ASNs they belong to.

While some of the visualized data was pretty obvious from the data table (nigh consecutive IP addresses in some cases), seeing the malicious clusters (per ASN) was (to me) pretty interesting. I don’t perform malicious host/network analysis as part of the day job, so the apparent clustering (and, also the “disconnected” ones) may not be interesting to anyone but me, but it gave me a practical example to test for the library I’m working on and may be interesting to others. It also shows you can make pretty graphs with R.

I’ve got the crufty R code up on github now and will keep poking at it as I have time. I’ll add the code that made the above image to the repository over the weekend.

Cover image from Data-Driven Security
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