# A Novel Hierarchical Intrusion Detection System based on Decision Tree and Rules-based Models

## TL;DR

This paper introduces a hierarchical intrusion detection system combining decision tree and rules-based classifiers, demonstrating improved accuracy and efficiency on the CICIDS2017 dataset.

## Contribution

It presents a novel hierarchical IDS that integrates multiple classifiers, enhancing detection performance over existing methods.

## Key findings

- Higher accuracy and detection rate
- Lower false alarm rate
- Reduced time overhead

## Abstract

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.