An analysis of the Karp-Rabin string matching algorithm
Information Processing Letters
Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Towards highly reliable enterprise network services via inference of multi-level dependencies
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Automated Rule-Based Diagnosis through a Distributed Monitor System
IEEE Transactions on Dependable and Secure Computing
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Detailed diagnosis in enterprise networks
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Characteristic analysis of internet traffic from the perspective of flows
Computer Communications
Fault detection in IP-based process control networks using data mining
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Detecting network faults on industrial process control IP networks
IPOM'07 Proceedings of the 7th IEEE international conference on IP operations and management
Understanding network failures in data centers: measurement, analysis, and implications
Proceedings of the ACM SIGCOMM 2011 conference
Fault Detection and Diagnosis in IP-Based Mission Critical Industrial Process Control Networks
IEEE Communications Magazine
Measurement analysis of IP-based process control networks
APNOMS'07 Proceedings of the 10th Asia-Pacific conference on Network Operations and Management Symposium: managing next generation networks and services
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Industrial control networks (ICNs) and systems support robust communications of devices in process control or manufacturing environments. ICN proprietary protocols are being migrated to Ethernet/IP networks in order to merge various different types of networks into a single common network. ICNs are deployed in mission-critical operations, which require a maximum level of network stability. Network stability is often described using several categories of network performance quality-of-service metrics, such as throughput, delay, and loss measurements. The question arises as to whether these network performance metrics are sufficient to run valuable diagnostics of ICN components and their communications. Any abnormal decision with respect to typical IP traffic behavior does not necessarily coincide with ICN fault cases. A precise and specific diagnostic technique for ICNs is required to remove the uncertainty in detecting problems. However, existing Ethernet/IP diagnosis tools have not been able to fully handle fault symptoms and mainly focus on network diagnostics rather than process or device diagnostics. This paper demonstrates that the absence of advanced fault diagnosis techniques leads to the development of new methodologies that are suitazble for ICN. We describe unique traffic characteristics and categorize the faults of ICN. We also propose a fault diagnosis, prediction, and adaptive decision methodologies, and verify them with real-world ICN data from the steel-making company POSCO. Our experience in developing the fault diagnosis system provides a firm guideline to understand the fault management mechanisms in large ICNs. Copyright © 2012 John Wiley & Sons, Ltd.