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Journals / Acta Infologica / 2025 / Volume: 9 Issue: 2

Detecting Robotic Cyber Attacks in Robot Operating System Networks

Hamdullah Karamollaoğlu(Electricity Generation Corporation)0000-0001-6419-2249
Pages
—
Publication Date
2025-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Using stratified 5-fold cross-validation, our results demonstrate that ensemble methods significantly outperform deep learning approaches in this context.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The increasing deployment of robotic systems in critical sectors such as manufacturing, healthcare, and infrastructure necessitates robust cybersecurity measures.
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Key Findings & Data: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.
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Conclusion & Impact:Using stratified 5-fold cross-validation, our results demonstrate that ensemble methods significantly outperform deep learning approaches in this context.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Robot operating system · cyber attacks · machine learning

Abstract

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.

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Cite This Article

Karamollaoğlu, H. (2025). Detecting Robotic Cyber Attacks in Robot Operating System Networks. Acta Infologica, Volume: 9 Issue: 2. https://doi.org/10.26650/acin.1798154

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