CASPER: Context-Aware Anomaly Detection System for Industrial Robotic Arms
- Citation Author(s):
-
Hakan Kayan
(Cardiff University)
Omer Rana (Cardiff University)Pete Burnap (Cardiff University)Charith Perera (Cardiff University) - Submitted by:
- Hakan KAYAN
- Last updated:
- DOI:
- 10.21227/q2e6-t883
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- Research Article Link:
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Abstract
Industrial cyber-physical systems (ICPS), which is the backbone of Industry 4.0, are the result of adapting emerging information communication technologies (ICT) to the industrial control systems (ICS). ICPS utilize autonomous robotic arms to accomplish manufacturing tasks. These arms follow a certain predetermined trajectory during the task.
In this dataset, we present four files generated from a setup that contains two Universal Robot UR3e collaborative robotic arms:
- A .csv file that consists of accelerometer, gyroscope, and magnetometer data of an arm that accomplishes a repetitive task, captured via Nicla Sense ME.
- Two .csv files (one per industrial arm) that consist of built-in arm parameters such as joint current, and velocity values etc.
- A .pcap file that contains the TCP/IP traffic between the arms and the controller PC.
We modified the joint velocity of the right arm during the experiment to simulate a cyberattack that causes physical deviations while the left arm runs normal. The whole experiment is 24 hours.
Instructions:
Files are ready to be used. Extract the .zip file to access to dataset.
The detailed analysis of the dataset is given on the paper with regarding GitHub code repository.
We will add the GitHub link here after the conference.
