| Patent application number | Description | Published |
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| 20090190511 | Method and apparatus for detecting wireless data subscribers using natted devices - A system and method for network based detection of wireless data subscribers using network address translation devices is provided. The method includes identifying a minimum number of devices showing the same internet protocol address. Packet identification sequences may include port numbers or internet protocol identification numbers. The method continues with grouping these applications by their packet identification sequences and applying detection logic where detection logic yields a conclusion that there are multiple host computers when a set of applications appears in a plurality of packet identification sequences. This method is particularly useful when internet protocol addresses are dynamic, as opposed to static. This method overcomes previous embodiments known in the art by being able to account for and work with live traffic, which enables real time detection. | 07-30-2009 |
| 20090268623 | EFFICIENT PROBABILISTIC COUNTING SCHEME FOR STREAM-EXPRESSION CARDINALITIES - In one embodiment, a method of monitoring a network. The method includes, at each node of a fixed set, constructing a corresponding vector of M components based on data packets received at the node during a time period, M being an integer greater than 1, the fixed set being formed of some nodes of the network; and, based on the constructed vectors, estimating how many of the received data packets have been received by all of the nodes of the set or estimating how many flows of the received data packets have data packets that have passed through all of the nodes of the set. The constructing includes updating a component of the vector of one of the nodes in response to the one of the nodes receiving a data packet. The updating includes selecting the component for updating by hashing a property of the data packet received by the one of the nodes. | 10-29-2009 |
| 20090271509 | PROBABILISTIC AGGREGATION OVER DISTRIBUTED DATA STREAMS - In one embodiment, a method of monitoring a network. The method includes, at each node of a set, constructing a corresponding vector of M components based on a stream of data packets received at the node during a time period, the set including a plurality of nodes of the network, M being greater than 1; and estimating a value of a byte traffic produced by a part of the packets based on the constructed vectors, the part being the packets received by every node of the set. The constructing includes updating a component of the vector corresponding to one of the nodes in response to the one of the nodes receiving a data packet. The updating includes selecting a component of the vector to be updated by hashing a property of the received data packet. | 10-29-2009 |
| 20090296594 | ESTIMATING CARDINALITY DISTRIBUTIONS IN NETWORK TRAFFIC - In one embodiment, a method of monitoring a network. The method includes: receiving, from each host of a set of two or more hosts of the network, a corresponding vector of M components constructed based on data packets received at the host during a time period, M being an integer greater than 1; and, based on the constructed vectors, using an expectation-maximization algorithm to estimate a cardinality distribution for the hosts in the set, wherein constructing a vector includes updating a component of the vector of the corresponding host in response to the corresponding host receiving a data packet, the updating including selecting the component for updating by hashing one or more fields of the data packet received by the corresponding host. | 12-03-2009 |
| 20100299287 | Monitoring time-varying network streams using state-space models - In one embodiment, a statistical model is generated based on observed data, the observed data being associated with a network device, online parameter fitting is performed on parameters of the statistical model, and for each newly observed data value, a forecast value is generated based on the statistical model, the forecast value being a prediction of a next observed data value, a forecasting error is generated based on the forecast value and the newly observed data value, and whether the data of the network stream is abnormal is determined based on a log likelihood ratio test of the forecasting errors and a threshold value. | 11-25-2010 |
| 20110010327 | METHOD AND APPARATUS FOR INCREMENTAL TRACKING OF MULTIPLE QUANTILES - A method and apparatus for incremental tracking of multiples quantiles is provided. A method for performing an incremental quantile update using a data value of a received data record includes determining an initial distribution function, updating the initial distribution function to form a new distribution function based on the received data value, generating an approximation of the new distribution function, and determining new quantile estimates from the approximation of the new distribution function. The initial distribution function includes a plurality of initial quantile estimates and a respective plurality of initial probabilities. The initial distribution function is updated to form the new distribution function based on the received data value. The new distribution function includes a plurality of quantile points identifying the respective initial quantile estimates and a respective plurality of new probabilities associated with the respective initial quantile estimates. The approximation of the new distribution function is generated by, for each pair of adjacent quantile points in the new distribution function, connecting the adjacent quantile points using a linear approximation of a region between the adjacent quantile points. The new quantile estimates and the new probabilities associated with the new quantile estimates may then be stored. | 01-13-2011 |
| 20110010337 | METHOD AND APPARATUS FOR INCREMENTAL QUANTILE TRACKING OF MULTIPLE RECORD TYPES - A method and apparatus are provided for incrementally tracking quantiles in the presence of multiple record types. A method for performing incremental quantile tracking includes receiving a first data record of a first record type having a first data value, determining whether a second data record of a second record type is received, determining an initial distribution function, updating the initial distribution function to form a new distribution function based on the first data value and whether a second data record is received, generating an approximation of the new distribution function, determining at least one new quantile estimate associated with at least one new probability of the new distribution function using the approximation of the new distribution function, and storing the at least one new quantile estimate and the at least one new probability associated with the at least one new quantile estimate. | 01-13-2011 |
| 20110069632 | TRACKING NETWORK-DATA FLOWS - A network-equipment-implemented method and apparatus for tracking durations of flows received at a network node in consecutive intervals utilizes two counting bloom filters in ping-pong operation to reduce memory and processing. Identifiers for flows that exceed a predetermined duration or number of intervals are stored in a long-duration flow-identifier table. Hash functions used within the counting bloom filters and optionally used in the long-duration flow-identifier table are chosen to minimize the probability of false positives in the detection of long-duration flows. In some embodiments, flows are sampled to conserve memory and processing resources at the risk of missing detection of some long-duration flows. | 03-24-2011 |
