Detecting anomalies in metro systems

Detecting anomalies in metro systems
Title:
Detecting anomalies in metro systems
Other Titles:
2018 IEEE 4th World Forum on Internet of Things (WF-IoT)
DOI:
10.1109/WF-IoT.2018.8355146
Publication Date:
05 February 2018
Citation:
M. H. A. Wibowo, H. Guo and W. L. Goh, "Detecting anomalies in metro systems," 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, Singapore, 2018, pp. 302-307. doi: 10.1109/WF-IoT.2018.8355146
Abstract:
In an autonomous vehicle system, a secure control between vehicles and instruments is critical. Particularly, in a metro or underground system, the control is done through a Supervisory Control and Data Acquisition (SCADA) network which is wired and close system. However, this does not imply that the system is safe from attack as an attack can come from insider by sending more command or less command. This attack can be detected by comparing the features extracted from the traffic happening to the heuristic and proper data set. The comparison done is not only by comparing the distribution of the data transferred but also looking at the correlation between each instrument. The correlation is needed since several instruments might work dependently while others might work independently. Data that are compared in the analysis are the features of each instrument from the traffic which are number of command transfer, number of handshake transfer, and the ratio of command transfer to the command transfer median from the samples. These three features are then analyzed and the results will show whether there is an anomaly in a certain period.
License type:
PublisherCopyrights
Funding Info:
National Research Foundation (NRF), Prime Minister’s Office, Singapore
Description:
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
978-1-4673-9944-9
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