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