Data-Driven Attack Anomaly Detection in Public Transport Networks

Data-Driven Attack Anomaly Detection in Public Transport Networks
Title:
Data-Driven Attack Anomaly Detection in Public Transport Networks
Other Titles:
2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)
Publication Date:
28 August 2019
Citation:
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.
License type:
PublisherCopyrights
Funding Info:
National Research Foundation (NRF), Prime Minister’s Office, Singapore, under its National Cybersecurity R&D Programme (Award No. NRF2014NCR-NCR001-31)
Description:
© 2019 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.
ISBN:
978-1-7281-1204-6
978-1-7281-1203-9
978-1-7281-1205-3
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