IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 2 2024
DOI: 10.56201/ijcsmt.v10.no2.2024.pg141.156

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A Distributed Intrusion Detection System for IoT-Enabled Network and Devices using Hybrid Technique

Davies, I. N, and Taylor, O. E., Anireh V.I.E., Bennett E.O.

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Abstract

The advent of the Internet-of-Things (IoT) has revolutionized the realm of contemporary computing and networking. IoT-enabled devices which include smart home appliances and industrial sensors, have become extremely common, allowing for effortless connection and data sharing across different fields. However, in recent years, the rapid proliferation of IoT devices has created significant security challenges, necessitating robust and efficient intrusion detection and prevention systems. This study proposes a novel Distributed Intrusion Detection System (DIDS) for IoT-enabled networks and devices using a hybrid technique. The system integrates Case-Based Reasoning (CBR) as the primary detection engine with a Neuro-Fuzzy Inference System (NFIS) for tuning unknown traffic analysis, forming a Hybrid Case-Based Neuro-Fuzzy System (HCBNFS). Additionally, the system incorporates Elliptic Curve Cryptography (ECC) for device authentication and privacy preservation. The DIDS model was designed using the Object-Oriented Design Approach and evaluated using the CIC-IoT2022 dataset and a synthetic smart home dataset. The system achieved high performance metrics, including 99% accuracy and 99.5% precision, recall, and F1-score. This research contributes to enhancing cybersecurity in IoT environments by addressing the unique challenges posed by their distributed and heterogeneous nature, offering improved scalability, fault tolerance, and adaptability compared to traditional centralized intrusion detection systems.

keywords:

Case-based Reasoning (CBR); Neuro-Fuzzy Inference System (NFIS); Elliptic Curve

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