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

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.

Citation

Ferrag, M. A., Maglaras, L., Ahmim, A., Derdour, M., & Janicke, H. (2020). RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks. Future Internet, 12(3), Article 44. https://doi.org/10.3390/fi12030044

• 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