Machine Learning for DDoS Attack Detection in Industry 4.0 CPPSs

Firooz B. Saghezchi*, Georgios Mantas, Manuel A. Violas, A. Manuel de Oliveira Duarte, Jonathan Rodriguez

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

19 Wedi eu Llwytho i Lawr (Pure)


The Fourth Industrial Revolution (Industry 4.0) has transformed factories into smart Cyber-Physical Production Systems (CPPSs), where man, product, and machine are fully interconnected across the whole supply chain. Although this digitalization brings enormous advantages through customized, transparent, and agile manufacturing, it introduces a significant number of new attack vectors—e.g., through vulnerable Internet-of-Things (IoT) nodes—that can be leveraged by attackers to launch sophisticated Distributed Denial-of-Service (DDoS) attacks threatening the availability of the production line, business services, or even the human lives. In this article, we adopt a Machine Learning (ML) approach for network anomaly detection and construct different data-driven models to detect DDoS attacks on Industry 4.0 CPPSs. Existing techniques use data either artificially synthesized or collected from Information Technology (IT) networks or small-scale lab testbeds. To address this limitation, we use network traffic data captured from a real-world semiconductor production factory. We extract 45 bidirectional network flow features and construct several labeled datasets for training and testing ML models. We investigate 11 different supervised, unsupervised, and semi-supervised algorithms and assess their performance through extensive simulations. The results show that, in terms of the detection performance, supervised algorithms outperform both unsupervised and semi-supervised ones. In particular, the Decision Tree model attains an Accuracy of 0.999 while confining the False Positive Rate to 0.001.
Iaith wreiddiolSaesneg
Rhif yr erthygle602
Tudalennau (o-i)e602
Nifer y tudalennau14
Rhif cyhoeddi4
Dyddiad ar-lein cynnar16 Chwef 2022
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsE-gyhoeddi cyn argraffu - 16 Chwef 2022

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