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RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks

Ferrag, Mohamed Amine; Maglaras, Leandros; Ahmim, Ahmed; Derdour, Makhlouf; Janicke, Helge

Authors

Mohamed Amine Ferrag

Ahmed Ahmim

Makhlouf Derdour

Helge Janicke



Abstract

This paper proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS 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 and BoT-IoT 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.

Journal Article Type Article
Acceptance Date Feb 27, 2020
Online Publication Date Mar 2, 2020
Publication Date 2020
Deposit Date Jan 6, 2023
Publicly Available Date Jan 9, 2023
Journal Future Internet
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 12
Issue 3
Article Number 44
DOI https://doi.org/10.3390/fi12030044
Keywords intrusion detection; IDS; hybrid IDS; learning machine; hierarchical; network security
Public URL http://researchrepository.napier.ac.uk/Output/2969541

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