A Proposed Model To Use ID3 Algorithm In The Classifier of A Network Intrusion Detection System

Classifiers of the contemporary network intrusion detection systems do not use any inductive learning technique to take inferences from the available independent data to arrive at a conclusion for classification of unknown threats. This makes the systems vulnerable to new attacks. The author proposes a model to embed primitive intelligence in the network intrusion detection systems. This model is based on Quinlain ID3 algorithm of decision tree construction and inductive learning. This model can be very useful to detect unknown attacks because it develops an optimized decision tree from available training set and can takes inference from the known (test) data to classify unknown patterns by adding new rules in the rule set