  This paper proposes a novel intrusion detection system (IDS) that combines
different classifier approaches which are based on decision tree and
rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA.
Specifically, the first and second method take as inputs features of the data
set, and classify the network traffic as Attack/Benign. The third classifier
uses features of the initial data set in addition to the outputs of the first
and the second classifier as inputs. The experimental results obtained by
analyzing the proposed IDS using the CICIDS2017 dataset, attest their
superiority in terms of accuracy, detection rate, false alarm rate and time
overhead as compared to state of the art existing schemes.
