Measured capacity of an Ethernet: myths and reality
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
Computer networks
Fault detection in an Ethernet network via anomaly detectors
Fault detection in an Ethernet network via anomaly detectors
The use of multi-dimensional parametric behavior of a CSMA/CD network for network diagnosis
The use of multi-dimensional parametric behavior of a CSMA/CD network for network diagnosis
Characteristics of network traffic flow anomalies
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Statistical Detection of Enterprise NetworkProblems
Journal of Network and Systems Management
Adaptive Anomaly Detection in Transaction-Oriented Networks
Journal of Network and Systems Management
A signal analysis of network traffic anomalies
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
A Distributed and Reliable Platform for Adaptive Anomaly Detection in IP Networks
DSOM '99 Proceedings of the 10th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management: Active Technologies for Network and Service Management
Sketch-based change detection: methods, evaluation, and applications
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
An Architecture for Inter-Domain Troubleshooting
Journal of Network and Systems Management
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Detection and identification of network anomalies using sketch subspaces
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Proceedings of the 44th annual Southeast regional conference
Failure Detection in Large-Scale Internet Services by Principal Subspace Mapping
IEEE Transactions on Knowledge and Data Engineering
Computing the fault tolerance of multi-agent deployment
Artificial Intelligence
Relaxed maximum a posteriori fault identification
Signal Processing
Asymmetric Feature Selection for BGP Abnormal Events Detection
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Forecasting-based sampling decision for accurate and scalable anomaly detection
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Real-time detection of traffic anomalies in wireless mesh networks
Wireless Networks
Review: Artificial intelligence approaches to network management: recent advances and a survey
Computer Communications
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In an Ethernet network, a common type of failure is the temporary of extended loss of bandwidth, or soft failure as it is referred to in the literature. Though the causes of soft failures vary, to the network user such failures are perceived as noticeably degraded or anomalous performance.This work uses anomaly detection as a means to signal performance degradations that are indicative of network soft failures. Detection is done via a signature matching mechanism, call a fault feature vector, which will detect the occurrence of a fault by looking for anomaly conditions particular to the fault. In a two-year study of the Carnegie Mellon University Computer Science Network the fault feature vector mechanism proved effective in detecting faults and discriminating between faults types. This mechanism was also effective at abstracting large amounts of network data to only those events which warranted operator attention; in this two-year study, over 32 million monitored data points were reduced to under a two hundred event matchings.