Dealing with highly variable feature set size
Before thinking of what type of algorithm you could use, I would think of how to properly preprocess your data.
Depending on how many possible values you might have for each of your 8 possible types (if I understood correctly), you could construct a 0's and 1's dataset, it is, indicating the presence or absence of each possible value in each event.
This would lead you to having a sparse matrix, but that is something you can deal with with some tools, a possibly nice example is in this link
Conclusion:

I would first try to identify if there are fixed possible values for each one of your types (by creating groups of ips by region? are there a fixed number of possible .exe, urls...?)
preprocess your data, so that http://google.com and google.com are the same value (i.e., by deleting the http:// from urls)
if you think the number of possible values is not ridiculously big, you might try to go for constructing the sparse matrix

After this, you could think of wich kind of algorithm to apply, and not going crazy for a neural network from the beginning.