2 weeks ago - last edited 2 weeks ago
I am a 4th year student trying to do an experiment comparing signature based and anomaly based detection methods. I would like to do this using decision trees and random forest algorithms. The end goal would be to measure the rate of false positives in both methods and to conclude with which is better for deterring cyber attacks.
I am not too sure how I am going to undertake this experiment but RM looks very helpful. I have publicly available security logs to use as data, have downloaded the anomaly detection extension, text mining extension and have set up an IDS system in Sec Onion to monitor my network.
Any advice/solutions would be much-appreciated apologises if ive not made what im doing clear enough I am far from an expert on the subject
2 weeks ago
So the first thing I would ask is if those logs you have are labeled. Do they have a tag for "intrusion" or "no intrusion"?
I would then load the data and use a Process Documents by Data operator and embed Tokenization/TransformCases/etc inside and then create the TFIDF word vectors and do a Cross Validation with maybe a Naive Bayes algo inside. You would have to ouput the PER port to see how well the model classifiies this data (a confusion matrix will be generated).
a week ago
Yes I have my data labelled as 'attack' or 'normal'. Will cross referencing with naive bayes give me the desired result of determining rate of false positives/classification accuracy etc? Additionally, I have a signature database containing a list of known attacks, do i have to manually train the model or something or does RM handle that?