| Title | Data Analysis for Anomaly Detection to Secure Rail Network |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Guo, H., Shen, X., Goh, W. L., Zhou, L. |
| Conference Name | 2018 International Conference on Intelligent Rail Transportation (ICIRT) |
| Keywords | alert function, anomaly detection, Bidirectional control, compositionality, Data analysis, Intelligent Data and Security, Intelligent Data Security, IP networks, network features, network flow, packet analysis system, Protocols, pubcrawl, Rail Network Security, rail network traffic data, rail systems, Rails, railway communication, Resiliency, Scalability, secure rail network, Support vector machines, telecommunication security, telecommunication traffic, timely detection, Wireshark detection |
| Abstract | The security, safety and reliability of rail systems are of the utmost importance. In order to better detect and prevent anomalies, it is necessary to accurately study and analyze the network traffic and abnormal behaviors, as well as to detect and alert any anomalies if happened. This paper focuses on data analysis for anomaly detection with Wireshark and packet analysis system. An alert function is also developed to provide an alert when abnormality happens. Rail network traffic data have been captured and analyzed so that their network features are obtained and used to detect the abnormality. To improve efficiency, a packet analysis system is introduced to receive the network flow and analyze data automatically. The provision of two detection methods, i.e., the Wireshark detection and the packet analysis system together with the alert function will facilitate the timely detection of abnormality and triggering of alert in the rail network. |
| DOI | 10.1109/ICIRT.2018.8641555 |
| Citation Key | guo_data_2018 |
Data Analysis for Anomaly Detection to Secure Rail Network