C4.5: programs for machine learning
C4.5: programs for machine learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The Architecture of NG-MON: A Passive Network Monitoring System for High-Speed IP Networks
DSOM '02 Proceedings of the 13th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management: Management Technologies for E-Commerce and E-Business Applications
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Issues of Control Networks when Introducing IP
SAINT-W '05 Proceedings of the 2005 Symposium on Applications and the Internet Workshops
Fault Detection and Diagnosis in IP-Based Mission Critical Industrial Process Control Networks
IEEE Communications Magazine
NEPnet: a scalable monitoring system for anomaly detection of network service
Proceedings of the 7th International Conference on Network and Services Management
An approach for failure recognition in IP-based industrial control networks and systems
International Journal of Network Management
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Industrial process control IP networks support communications between process control applications and devices. Communication faults in any stage of these control networks can cause delays or even shutdown of the entire manufacturing process. The current process of detecting and diagnosing communication faults is mostly manual, cumbersome, and inefficient. Detecting early symptoms of potential problems is very important but automated solutions do not yet exist. Our research goal is to automate the process of detecting and diagnosing the communication faults as well as to prevent problems by detecting early symptoms of potential problems. To achieve our goal, we have first investigated real-world fault cases and summarized control network failures. We have also defined network metrics and their alarm conditions to detect early symptoms for communication failures between process control servers and devices. In particular, we leverage data mining techniques to train the system to learn the rules of network faults in control networks and our testing results show that these rules are very effective. In our earlier work, we presented a design of a process control network monitoring and fault diagnosis system. In this paper, we focus on how the fault detection part of this system can be improved using data mining techniques.