Abstract: A system for detecting and managing network data traffic and a network load traffic management module is described. The system for detecting and managing network data traffic includes a Radio Access Network (RAN), a plurality of mobile devices, a network load traffic management server, a first dataset and a corresponding first baseline, a second data set and a corresponding second baseline, a data flow dataset, and a probability for one or more mobile devices to remain within each location associated with each RAN site, wherein the probability is calculated by the network traffic management module.

Abstract: A system for detecting a computing device tethered to a mobile device is described. The system includes communicatively coupled the computing device to the mobile device with an IPv4 domain protocol. Additionally, the mobile device is communicatively coupled to the mobile data network with an IPv6 domain protocol. A network module, associated with a mobile data network, receives a DNS request packet from the mobile device. An IPv6 hostname and an IPv6 destination address is associated with the DNS response packet, when the DNS response packet is associated with the IPv6 address. Additionally, an IPv4 hostname and an IPv4 destination address is associated with the DNS response packet, when the DNS response packet is associated with the IPv4 address. A communication packet, generated by the mobile device, is also received by the network module. The communication packet includes a destination address and a hostname.

Abstract: A non-rule based security detection system and method is described. The method includes identifying a plurality of data sources. The method then proceeds to generate a baseline for each data source. The baseline includes a plurality of data source outputs that are evaluated over a time period. A plurality of data source anomalies are detected, in which each data source anomaly is associated with at least one data source output exceeding a threshold for the data source baseline. A geolocation for each data source anomaly is then identified. A plurality of correlations between the plurality of data source anomalies and the geolocation for each data source anomaly are generated. At least one correlation is associated with a security event.

Abstract: Some embodiments include a method and/or a system for collecting and managing network data. Other embodiments of related methods and systems are disclosed.

Abstract: The present method and system relate to categorizing URLs (Uniform Resource Locators) of web pages accessed by multiple users over an IP (Internet Protocol) based data network. The method and system collect real time data from IP data traffic occurring on the IP based data network, and extract parameters from the collected real time data, the parameters including an URL of a web page. The URL is processed by a rule based categorization engine, to associate a matching category to the URL of the web page. When no matching category is inferred, the URL is transferred to a semantic based categorization engine. A matching category is associated to the transferred URL by the semantic based categorization engine, based on a semantic analysis of the textual content extracted from the web page associated to the URL.

Abstract: Representative embodiments of a distributed processing method of facilitating interactive analytics of streaming data records by receiving the data records at a plurality of distributed computational nodes, establishing and storing dimensions corresponding to attributes of the data records, parsing the streaming data records to identify matches to at least one of the dimensions and based thereon, reducing the number of data records to create a targeted subset of the data, re-distributing the targeted subsets of the streaming data records among the distributed computational nodes in accordance with the dimensions stored on the nodes, updating a database storing measures of the dimensions in accordance with the targeted subsets of the streaming data records, and using the database to respond to a query based on measures associated with one or more of the dimensions.

Abstract: Representative embodiments of a distributed processing method of facilitating interactive analytics of streaming data records by receiving the data records at a plurality of distributed computational nodes, establishing and storing dimensions corresponding to attributes of the data records, parsing the streaming data records to identify matches to at least one of the dimensions and based thereon, reducing the number of data records to create a targeted subset of the data, re-distributing the targeted subsets of the streaming data records among the distributed computational nodes in accordance with the dimensions stored on the nodes, updating a database storing measures of the dimensions in accordance with the targeted subsets of the streaming data records, and using the database to respond to a query based on measures associated with one or more of the dimensions.

Abstract: The present relates to a method and a system for identifying an application type from encrypted traffic transported over an IP network. The method and system extract at least a portion of IP flow parameters from the encrypted traffic using at least one of specific target encryption types. Then, the method and system transmit the extracted IP flow parameters to a learning-based classification engine. The learning-based classification engine has been trained with unencrypted traffic. Then, the method and system infer at least one corresponding application type for the extracted IP flow parameters.

Abstract: A cardinality of an incoming data stream is maintained in real time; the cardinality is maintained in a data structure that is represented by an unsorted list at low cardinalities, a linear counter at medium cardinalities, and a PCSA at high cardinalities. The conversion to the linear counter makes use of the data in the unsorted list, after which that data is discarded. The conversion to the PCSA uses only the data in the linear counter.