Detecting anomalies of a cyber physical system (CPS), which is a complex system consisting of both physical and software parts, is important because a CPS often operates autonomously in an unpredictable environment.
However, because of the ever-changing nature and lack of a precise model for a CPS, detecting anomalies is still a challenging task.
To address this problem, we propose applying an outlier detection method to a CPS log.
By using a log obtained from an actual aquarium management system, we evaluated the effectiveness of our proposed method by analyzing outliers that it detected.
By investigating the outliers with the developer of the system, we confirmed that some outliers indicate actual faults in the system.
For example, our method detected failures of mutual exclusion in the control system that were unknown to the developer.
Our method also detected transient losses of functionalities and unexpected reboots.
On the other hand, our method did not detect anomalies that were too many and similar.
In addition, our method reported rare but unproblematic concurrent combinations of operations as anomalies.
Thus, our approach is effective at finding anomalies, but there is still room for improvement.
A log of CPS system, Aqua-tan, recorded from the beginning of system operation.
The log files contain id, timestamp, label, numeric value, and text value.
The “label” column denotes commands and events of Aqua-tan system.
This data is analyzed in our paper in IWESEP2017.
- Data: log.csv
- Y. Harada, Y. Yamagata, O. Mizuno, and E. Choi, "Log-Based Anomaly Detection of CPS Using a Statistical Method," In Proc. of the 8th IEEE International Workshop on Empirical Software Engineering in Practice (IWESEP2017), pp. 1-6, March 2017.
- Y. Harada, Y. Yamagata, O. Mizuno, and E. Choi, "Best Paper Award: a Log-Based Anomaly Detection of Cps Using a Statistical Method," In the 8th IEEE International Workshop on Empirical Software Engineering in Practice (IWESEP2017), March 2017.