Abstract: A network anomaly detector evaluates two complementary measurements of network statistics, a time variation and correlation among those statistics, to provide an extremely robust detection of network anomalies. In one embodiment, the variability and correspondence are compared against historically derived thresholds to provide for a system that accommodates to local network conditions and evolving network qualities.

Abstract: A method for characterizing a sample includes acquiring a trace signal for the sample. A set of configurations is generated for defining modeling signals to model the trace signal. Each modeling signal is defined by a plurality of model parameters, and each configuration represents an associated modeling signal having a locally optimal score for fitting the trace signal. A classification cluster is defined in a parameter domain defined by the plurality of model parameters. The classification cluster has an associated class type. The sample is determined to have the class type associated with the classification cluster responsive to determining that at least one of the configurations in the set has a distance from the classification cluster less than a threshold.

Abstract: A method and apparatus for detecting and localizing an anomaly for a network are disclosed. For example, the method sends a first set of probe packets on at least one path of the network, and detects a performance anomaly on a first path of the at least one path. The method then identifies at least one link on the first path that is responsible for the performance anomaly by applying a second set of probe packets.

Abstract: Performance for a network is measured by sending multi-objective probes on a path, receiving at least one of the multi-objective probes for the path, and determining performance measurements for at least two parameters of the path determined from the at least one of the multi-objective probes. Separate algorithms are simultaneously executed to measure the at least two parameters of the path determined from the at least one of the multi-objective probes.

Abstract: A method and an apparatus for providing a measurement of performance for a network are disclosed. For example, the method sends a plurality of multi-objective probes on a path, and receives one or more of said plurality of multi-objective probes for the path. The method then determines a plurality of performance measurements.

Abstract: A method and apparatus for detecting and localizing an anomaly for a network are disclosed. For example, the method sends a first set of probe packets on at least one path of the network, and detects a performance anomaly on a first path of the at least one path. The method then identifies at least one link on the first path that is responsible for the performance anomaly by applying a second set of probe packets.

Abstract: A network anomaly detector evaluates two complementary measurements of network statistics, a time variation and correlation among those statistics, to provide an extremely robust detection of network anomalies. In one embodiment, the variability and correspondence are compared against historically derived thresholds to provide for a system that accommodates to local network conditions and evolving network qualities.

Abstract: A method and an apparatus for providing a measurement of performance for a network are disclosed. For example, the method sends a plurality of multi-objective probes on a path, and receives one or more of said plurality of multi-objective probes for the path. The method then determines a plurality of performance measurements.