Enhancing IIOT security with machine learning and deep learning for intrusion detection
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
University Of Malaya
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The rapid growth of the Internet of Things (IoT) and digital industrial devices has significantly impacted various aspects of life, underscoring the importance of the Industrial Internet of Things (IIoT). Given its importance in industrial contexts that affect human life, the IIoT represents a key subset of the broader IoT landscape. Due to the proliferation of sensors in smart devices, which are viewed as points of contact, as the gathering of data and information regarding the IIoT systems and devices operating on the IoT, there is an urgent requirement for developing effective security methods to counter such threats as well as protecting IIoT systems. In this study, we develop and evaluate a well -optimized intrusion detection system (IDS) based on deep learning (DL) and machine learning (ML) techniques to enhance IIoT security. Leveraging the Edge-IIoTset dataset, specifically designed for IIoT cybersecurity evaluations, we focus on detecting and mitigating 14 distinct attack types targeting IIoT and IoT protocols. These attacks are categorized into five threat groups: information collection, malware, DDoS, manin -the -middle attacks, and injection attacks. We conducted experiments using machine learning algorithms (knearest neighbors, decision tree) and a neural network on the KNIME platform, achieving a remarkable 100% accuracy with the decision tree model. This high accuracy demonstrates the effectiveness of our approach in protecting industrial IoT networks.
Açıklama
Anahtar Kelimeler
Deep Learning, IIoT, Cybersecurity, Intrusion Detection System, Machine Learning, KNIME
Kaynak
Malaysian Journal of Computer Science
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
37
Sayı
2
Künye
Awad, O.F., Hazim, L.R., Jasim, A.A., Ata, O. (2024). Enhancing IIOT security with machine learning and deep learning for intrusion detection. Malaysian Journal of Computer Science, 37(2), 139-153.
