Enhancing Intrusion Detection in Industrial Internet of Things through Automated Preprocessing

• Autorzy: Sezgin Anıl , Boyacı Aytuğ

Identyfikatory

DOI

10.12913/22998624/162004

• Języki publikacji: EN

Abstrakty

EN

Industrial Internet of Things (IIoT) is a rapidly growing field, where interconnected devices and systems are used to improve operational efficiency and productivity. However, the extensive connectivity and data exchange in the IIoT environment make it vulnerable to cyberattacks. Intrusion detection systems (IDS) are used to monitor IIoT networks and identify potential security breaches. Feature selection is an essential step in the IDS process, as it can reduce computational complexity and improve the accuracy of the system. In this research paper, we propose a hybrid feature selection approach for intrusion detection in the IIoT environment using Shapley values and a genetic algorithm-based automated preprocessing technique which has three automated steps including imputation, scaling and feature selection. Shapley values are used to evaluate the importance of features, while the genetic algorithm-based automated preprocessing technique optimizes feature selection. We evaluate the proposed approach on a publicly available dataset and compare its performance with existing state-of-the-art methods. The experimental results demonstrate that the proposed approach outperforms existing methods, achieving high accuracy, precision, recall, and F1-score. The proposed approach has the potential to enhance the performance of IDS in the IIoT environment and improve the overall security of critical industrial systems.

Słowa kluczowe

EN

feature selection; intrusion detection; machine learning; industrial internet of things; Internet of things; IIoT

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2023
• Tom: Vol. 17, no 2
• Strony: 120--135
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Sezgin Anıl

• ATASAREN, National Defence University, Konaklar, Yenilevent, Org. İzzettin Aksalur Cd., 34334 Beşiktaş, Istanbul, Turkey
• Siemens Corporate Technology, Esentepe, Yakacık Yolu No:111, 34870 Kartal, Istanbul, Turkey

• https://orcid.org/0000-0002-5754-1380

autor

Boyacı Aytuğ

• Department of Computer Engineering, National Defence University, Air Force Academy, Istanbul, Turkey

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).