background: I am using KDD99 data set with weka library to predict the IDS attacks the training and all works fine its around 42 features based on which the attack predication works. But in a real time environment when i use the Sniffer to capture the packets i may not be able to fetch all the 42 features from the packet not it would be required as well. I would be getting around 10 features.
I am new to data mining and weka library Now the problem is i would have used all the 42 features from training data set to train network and the i have 10 features in the test data.
Do i need to train the network with only 10 of these features which are going to get captured or is there a way i can train the network with 42 features and while classification i can request to consider the only 10 features is there a way to make attribute selection during the classification of data?
Can any one share me the Java snippet code if there is any solution.
The alert for the outdated KDD99 is useful and many thanks for it but still i was thinking what if i have less no. of features in Test data than training data how to address the problem? what should be the ideal way to solve in weka
Thanks in advance....