Data-Driven Attack Anomaly Detection in Public Transport Networks

Data-Driven Attack Anomaly Detection in Public Transport Networks

2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

10.1109/VTS-APWCS.2019.8851637

https://doi.org/10.1109/VTS-APWCS.2019.8851637

Rui Yin, Nicholas Heng Loong Wong, Huaqun Guo, Wang Ling Goh

Intrusion detection, self-organizing map, clustering, ensemble learning, transport networks

28 August 2019

Y. Rui, N. H. L. Wong, H. Guo and W. L. Goh, "Data-Driven Attack Anomaly Detection in Public Transport Networks," 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS), Singapore, 2019, pp. 1-5.

We present a method for attack detection in public transport networks. Through unsupervised machine learning, the daily data of the transportation system is clustered and a training model is established. Improved accuracy is achieved through self-organizing mapping and ensemble learning. We then apply the clustering model to assess the performance of the attack anomaly detection.

PublisherCopyrights

National Research Foundation (NRF), Prime Minister’s Office, Singapore, under its National Cybersecurity R&D Programme (Award No. NRF2014NCR-NCR001-31)

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