Detecting Robotic Cyber Attacks in Robot Operating System Networks

Yıl 2025, Cilt: 9 Sayı: 2, 682 - 701, 31.12.2025

Hamdullah Karamollaoğlu

https://doi.org/10.26650/acin.1798154

https://izlik.org/JA29LA62PT

Öz

The increasing deployment of robotic systems in critical sectors such as manufacturing, healthcare, and infrastructure necessitates robust cybersecurity measures. The Robot Operating System (ROS), a core middleware in modern robotics, is inherently susceptible to cyber threats due to its lack of integrated security mechanisms. This study presents a comprehensive benchmark evaluation of intrusion detection solutions for ROS-based environments. Utilizing the novel ROSIDS23 dataset, which includes realistic attack scenarios—such as Denial-of-Service (DoS), Unauthorized Publish, Unauthorized Subscribe, and Subscriber Flood—we rigorously evaluated and compared fifteen state-of-the-art Machine Learning (ML) and Deep Learning (DL) models. Using stratified 5-fold cross-validation, our results demonstrate that ensemble methods significantly outperform deep learning approaches in this context. Gradient Boosting achieved the highest performance, with 99.80% accuracy, precision, recall, and F1-score, followed by Light Gradient Boosting Machine (LightGBM) at 99.51% and Extreme Gradient Boosting (XGBoost) at 99.48%. Among DL models, the best-performing One-Dimensional Convolutional Neural Network (1D-CNN) reached 98.55%. Beyond overall metrics, we examine per-class performance, confusion matrices, and Receiver Operating Characteristic (ROC) curves, highlighting model-specific strengths and weaknesses, particularly in detecting minority attack classes.

Anahtar Kelimeler

Robot operating system , cyber attacks , machine learning , deep learning , robotic cybersecurity

Kaynakça

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