Work

Proposal for a new architecture for detecting intrusion clusters in IoTs

Year: 2024

Type: article

Abstract: In this paper, we are proposing a new architecture to detect intrusions in an IoT environment. To achieve this, we reviewed the various works to our knowledge, relating to intrusions in IoT. As a contribution, our proposed architecture has 5 components (sensors - data storage - preprocessing and associated classification algorithms). Also To provide intelligence and evaluate the performance of our architecture, we initially implemented in our architecture the DBSCAN and K-means algorithms separately. Secondly, we proceeded with a hybridization of these algorithms (DBSCAN and K-means) . In terms of results, this hybridization allowed us to use DBSCAN to identify dense clusters of arbitrary form of data as well as intrusions in this data. As for K-means, it made it possible to refine the globular clusters found by DBSCAN in order to best detect and predict the sources of intrusion. These results show that our solution makes it possible to detect intrusions in IoTs in an efficient manner compared to the 2 algorithms (DBSSAN AND K-means) applied separately in our architecture.

Source: International Journal of Engineering & Technology

DOI: 10.14419/j08qzt66

Authors: Kanga Koffi, BROU Aguié Pacôme Bertrand, Kamagaté Beman Hamidja

Institutions: École Supérieure Africaine des TIC, Institut National Polytechnique Félix Houphouët-Boigny

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4407595728

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