How to detect patterns in a data set of given IP addresses using a neural network? How to detect patterns in a data set of given IP addresses using a neural network?

The data set is actually a list of all the vulnerable devices on a network. I want to use a neural network that detctes any patters in the occurrences of these vulnerabilities with reference to their IPs and ports.
It seems that you want to detect ranges of IP addresses that are vulnerable/dangerous/etc, right? Such ranges are essentially numeric intervals, and so my suggestion is to look at decision tree learning instead of neural networks, because you are essentially doing a classification task where you want to test both categorical data and splits over numerical attributes.

The result will be a tree-like function (nested conditionals) of the form

IF ...&gt; address &gt; ... 
   THEN [vulnerable]
   ELSE IF port=... 
              THEN [not vulnerable]
              ELSE [vulnerable]


where a huge benefit is that it is also more human-readable than a neural net.

The most prominent algorithms for decision trees are ID 3 and its successor C4.5.