A Novel Hierarchical Intrusion Detection System based on Decision Tree and Rules-based Models
This addresses network security for organizations by improving intrusion detection, but it is incremental as it combines existing classifier methods.
The paper tackled intrusion detection by proposing a hierarchical system combining REP Tree, JRip, and Forest PA classifiers, achieving superior accuracy, detection rate, false alarm rate, and time overhead compared to state-of-the-art schemes on the CICIDS2017 dataset.
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.