how to split available data into training and testing (Information security) I was advised to ask my question here.

Recently, I made a post about finding suitable dataset for SIEM (Security Information and Event Management) systems. The goal was to work on classification and correlation to detect security attacks. 

I decided to use the dataset from the Honeynet Project Challenges. The problem is this: I don't know if I should use the whole dataset set for my project, because if you look, for example, at the KDD99 dataset, it is devised into two parts: 10% for training and 90% for testing.

I have seen some researcher use dataset A for training and dataset B for testing, do you have any other ideas? I am really stuck at the part of training vs. testing

If my question is too broad. I don't mind some reading materials that will help me deal with my dataset.

Bests,
You should check out k-fold cross validation techique. Its a quality control technique which makes sure, the prediction/classification done by a model would generalize well to unseen test data. The idea is to split the dataset into k times and use it recursively to train and cross validate your network which at end can be used on the test data to report the actual accuracy.

Here is a link for reference : 
https://machinelearningmastery.com/k-fold-cross-validation/