Data-Driven Attack Anomaly Detection in Public Transport Networks

被引：0

作者：

Rui, Yin ^{[1
]}

Wong, Nicholas Heng Loong

Guo, Huaqun ^{[2
]}

Goh, Wang Ling ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore

[2] Agcy Sci Technol & Res, Inst Infocomm Res, Singapore, Singapore

来源：

2019 IEEE VTS ASIA PACIFIC WIRELESS COMMUNICATIONS SYMPOSIUM (APWCS 2019) | 2019年

基金：

新加坡国家研究基金会;

关键词：

Intrusion detection; self-organizing map; clustering; ensemble learning; transport networks; INTRUSION;

D O I：

10.1109/vts-apwcs.2019.8851637

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页数：5

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