{"title":"基于modbus的油气井监测系统异常检测传感器","authors":"Xinchi He, Ethan Robards, R. Gamble, M. Papa","doi":"10.1109/ICDIS.2019.00008","DOIUrl":null,"url":null,"abstract":"Timely detection of network traffic anomalies in oil and gas wells is critical to support operations. This paper describes a network sensor that has been specifically designed to operate within an existing well-monitoring infrastructure. Network traffic and flow features are extracted in real-time and compared against pre-set and moving averages to detect and report anomalies. A prototype has been tested using the Modbus protocol and network traffic covering several months of operations. In order to avoid potential impact on the production environment, scripts captured network packets that were then replayed on the IMUNES network emulator. Preliminary results have identified useful metrics for anomaly detection in a production environment.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Anomaly Detection Sensors for a Modbus-Based Oil and Gas Well-Monitoring System\",\"authors\":\"Xinchi He, Ethan Robards, R. Gamble, M. Papa\",\"doi\":\"10.1109/ICDIS.2019.00008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Timely detection of network traffic anomalies in oil and gas wells is critical to support operations. This paper describes a network sensor that has been specifically designed to operate within an existing well-monitoring infrastructure. Network traffic and flow features are extracted in real-time and compared against pre-set and moving averages to detect and report anomalies. A prototype has been tested using the Modbus protocol and network traffic covering several months of operations. In order to avoid potential impact on the production environment, scripts captured network packets that were then replayed on the IMUNES network emulator. Preliminary results have identified useful metrics for anomaly detection in a production environment.\",\"PeriodicalId\":181673,\"journal\":{\"name\":\"2019 2nd International Conference on Data Intelligence and Security (ICDIS)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 2nd International Conference on Data Intelligence and Security (ICDIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDIS.2019.00008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDIS.2019.00008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Anomaly Detection Sensors for a Modbus-Based Oil and Gas Well-Monitoring System
Timely detection of network traffic anomalies in oil and gas wells is critical to support operations. This paper describes a network sensor that has been specifically designed to operate within an existing well-monitoring infrastructure. Network traffic and flow features are extracted in real-time and compared against pre-set and moving averages to detect and report anomalies. A prototype has been tested using the Modbus protocol and network traffic covering several months of operations. In order to avoid potential impact on the production environment, scripts captured network packets that were then replayed on the IMUNES network emulator. Preliminary results have identified useful metrics for anomaly detection in a production environment.