A message-based fault diagnosis procedure
SIGCOMM '86 Proceedings of the ACM SIGCOMM conference on Communications architectures & protocols
Fault detection in an Ethernet network using anomaly signature matching
SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
Applications of machine learning and rule induction
Communications of the ACM
Schemes for fault identification in communication networks
IEEE/ACM Transactions on Networking (TON)
Automatic alarm correlation for fault identification
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 2)-Volume - Volume 2
Fault Identification in Computer Network A Review and a New Approach
Fault Identification in Computer Network A Review and a New Approach
Anomaly detection in IP networks
IEEE Transactions on Signal Processing
International Journal of Network Management
BPMN pattern for agent-based simulation model representation
Proceedings of the Winter Simulation Conference
Agent-based conceptual model representation using BPMN
Proceedings of the Winter Simulation Conference
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Network fault management is concerned with the detection, isolation, and correction of anomalous conditions that occur in a computer network. Present state of art in fault management classifies existing methodologies into two main categories: reactive rule based approaches and intelligent monitoring systems. In this paper we explore the concept of anticipatory behavior to develop an intelligent agent-based network management model, which uses an anticipatory agent to proactively detect occurrence of faults using a predictive model pertaining to network performance. To compare the effectiveness of the anticipatory technique, we build a simulation model of a network using the DEVS framework. Two reactive rule based fault management strategies are compared against the anticipatory approach. Results of the comparative analysis are presented to demonstrate the potential of the anticipatory technique in detecting network anomalies.