Abstract:
Described is a system and method for determining a classification of an application that includes initiating a stress test on the application, the stress test including a predetermined number of stress events, wherein the stress events are based on a network impairment. A response by the application to each stress event is identified and the application is classified as a function of the response into one of a first classification and a second classification, the first classification indicative of a normal application and the second classification indicative of an undesired application. If, the application is in the second classification, a network response procedure is executed.
Abstract:
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for approximating an accent source. A system practicing the method collects data associated with customer specific services, generates country-specific or dialect-specific weights for each service in the customer specific services list, generates a summary weight based on an aggregation of the country-specific or dialect-specific weights, and sets an interactive voice response system language model based on the summary weight and the country-specific or dialect-specific weights. The interactive voice response system can also change the user interface based on the interactive voice response system language model. The interactive voice response system can tune a voice recognition algorithm based on the summary weight and the country-specific weights. The interactive voice response system can adjust phoneme matching in the language model based on a possibility that the speaker is using other languages.
Abstract:
A traffic classifier has a plurality of binary classifiers, each associated with one of a plurality of calibrators. Each calibrator trained to translate an output score of the associated binary classifier into an estimated class probability value using a fitted logistic curve, each estimated class probability value indicating a probability that the packet flow on which the output score is based belongs to the traffic class associated with the binary classifier associated with the calibrator. The classifier training system configured to generate a training data based on network information gained using flow and packet sampling methods. In some embodiments, the classifier training system configured to generate reduced training data sets, one for each traffic class, reducing the training data related to traffic not associated with the traffic class.
Abstract:
An efficient streaming method and apparatus for detecting hierarchical heavy hitters from massive data streams is disclosed. In one embodiment, the method enables near real time detection of anomaly behavior in networks.
Abstract:
Statistical methods are used to observe packet flow arrival processes and to infer routing changes from those observations. Packet flow arrivals are monitored using NetFlow or another packet flow monitoring arrangement. Packet flow arrivals are quantified by counting arrivals per unit time, or by measuring an inter-arrival time between flows. When a change in packet flow arrivals is determined to be statistically significant, a change in network routing protocol is reported.
Abstract:
A device includes a processor configured to determine a number of users in each of a plurality of wireless telephone cells of a trajectory in a wireless telephone network. The processor is also configured to determine handoff data between each adjacent pair of the wireless telephone cells, and to determine a first number of users traveling along the trajectory in the wireless telephone network while on a telephone call. The processor also calculates a total number of users associated with the trajectory in the wireless telephone network based on the handoff data between each adjacent pair of the wireless telephone cells, and based on the first number of users traveling along the trajectory while on the telephone call.
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 providing performance measurements on network tunnels in packet networks are disclosed. For example, the method establishes two tunnels between a first measurement host and a first router, and establishes a tunnel between the first router and a second measurement host. The method also establishes a multicast group having a plurality of members, and sends one or more packets addressed to the multicast group from the first measurement host. The method measures the frequencies of directly and/or indirectly received responses from the plurality of members of the multicast group, and provides a plurality of estimated values for a plurality of packet transmission rates from measurement of the frequencies for one or more of said tunnels.
Abstract:
The invention relates to streaming algorithms useful for obtaining summaries over unaggregated packet streams and for providing unbiased estimators for characteristics, such as, the amount of traffic that belongs to a specified subpopulation of flows. Packets are sampled from a packet stream and aggregated into flows and counted by implementation of Adaptive Sample-and-Hold (ASH) or Adaptive NetFlow (ANF), adjusting the sampling rate based on a quantity of flows to obtain a sketch having a predetermined size, the sampling rate being adjusted in steps; and transferring the count of aggregated packets from SRAM to DRAM and initializing the count in SRAM following adjustment of the sampling rate.
Abstract:
Disclosed herein are systems, computer-implemented methods, and computer-readable media for sampling network traffic. The method includes receiving a desired quantity of flow record to sample, receiving a plurality of network flow record each summarizing a network flow of packets, calculating a hash for each flow record of based on one or more invariant part of a respective flow, generating a quasi-random number from the calculated hash for each respective flow record, generating a priority from the calculated hash for each respective flow record, and sampling exactly the desired quantity of flow records, selecting flow records having a highest priority first. In one aspect, the method further partitions the plurality of flow records into groups based on flow origin and destination, generates an individual priority for each partitioned group, and separately samples exactly the desired quantity of flow records from each partitioned group, selecting flows having a highest individual priority first.