Abstract: The present disclosure provides method and system for tracking cellular devices by a processor configured to receiving communications via communications interface of cellular communication packets exchanged over a cellular network and fixed-network communication packets exchanged with a router connected to a fixed network; identifying, in the cellular communication packets, data items exchanged with a cellular device at a cellular-communication time. The method and system then identifies the data item in the fixed-network communication packets, and in response to identifying the data item in both the cellular communication packets and the fixed-network communication packets, and based on a difference between the cellular-communication time and a fixed-network-communication time at which the data item was exchanged with the router, calculate a likelihood that the cellular device was connected to the fixed network via the router at the fixed-network-communication time, and output the likelihood.

Abstract: The present disclosure provides method and system for a processor configured to receive, via a communication interface, cellular communication exchanged over at least one cellular network and fixed-network communication exchanged with a router connected to a fixed network. The method and system further correlating between the cellular communication and the fixed-network communication, and computing respective likelihoods for multiple cellular devices having connected to the fixed network via the router. In response to computing the likelihoods, compute one or more estimated properties associated with the router, and output the estimated properties.

Abstract: A traffic-monitoring system that monitors encrypted traffic exchanged between IP addresses used by devices and a network, and further receives the user-action details that are passed over the network. By correlating between the times at which the encrypted traffic is exchanged and the times at which the user-action details are received, the system associates the user-action details with the IP addresses. In particular, for each action specified in the user-action details, the system identifies one or more IP addresses that may be the source of the action. Based on the IP addresses, the system may identify one or more users who may have performed the action. The system may correlate between the respective action-times of the encrypted actions and the respective approximate action-times of the indicated actions. The system may hypothesize that the indicated action may correspond to one of the encrypted actions having these action-times.

Abstract: Machine learning techniques for classifying encrypted traffic with a high degree of accuracy. The techniques do not require decrypting any traffic and may not require any manually-labeled traffic samples. An automated system uses an application of interest to perform a large number of user actions of various types. The system further records, in a log, the respective times at which the actions were performed. The system further receives the encrypted traffic exchanged between the system and the application server, and records properties of this traffic in a time series. Subsequently, by correlating between the times in the log and the times at which the traffic was received, the system matches each of the user actions with a corresponding portion of the traffic, which is assumed to have been generated by the user action. The system thus automatically builds a labeled training set, which may be used to train a network-traffic classifier.

Abstract: Systems and methods for passive monitoring of computer communication that does not require performing any decryption. A monitoring system receives the traffic exchanged with each relevant application server, and identifies, in the traffic, sequences of messages—or “n-grams”—that appear to belong to a communication session between a pair of users. Subsequently, based on the numbers and types of identified n-grams, the system identifies each pair of users that are likely to be related to one another via the application, in that these users used the application to communicate (actively and/or passively) with one another. The system may identify those sequences of messages that, by virtue of the sizes of the messages in the sequence, and/or other properties of the messages that are readily discernable, indicate a possible user-pair relationship.

Abstract: A traffic-monitoring system that monitors encrypted traffic exchanged between IP addresses used by devices and a network, and further receives the user-action details that are passed over the network. By correlating between the times at which the encrypted traffic is exchanged and the times at which the user-action details are received, the system associates the user-action details with the IP addresses. In particular, for each action specified in the user-action details, the system identifies one or more IP addresses that may be the source of the action. Based on the IP addresses, the system may identify one or more users who may have performed the action. The system may correlate between the respective action-times of the encrypted actions and the respective approximate action-times of the indicated actions. The system may hypothesize that the indicated action may correspond to one of the encrypted actions having these action-times.

Abstract: Systems and methods for passive monitoring of computer communication that does not require performing any decryption. A monitoring system receives the traffic exchanged with each relevant application server, and identifies, in the traffic, sequences of messages—or “n-grams”—that appear to belong to a communication session between a pair of users. Subsequently, based on the numbers and types of identified n-grams, the system identifies each pair of users that are likely to be related to one another via the application, in that these users used the application to communicate (actively and/or passively) with one another. The system may identify those sequences of messages that, by virtue of the sizes of the messages in the sequence, and/or other properties of the messages that are readily discernable, indicate a possible user-pair relationship.

Abstract: A traffic-monitoring system that monitors encrypted traffic exchanged between IP addresses used by devices and a network, and further receives the user-action details that are passed over the network. By correlating between the times at which the encrypted traffic is exchanged and the times at which the user-action details are received, the system associates the user-action details with the IP addresses. In particular, for each action specified in the user-action details, the system identifies one or more IP addresses that may be the source of the action. Based on the IP addresses, the system may identify one or more users who may have performed the action. The system may correlate between the respective action-times of the encrypted actions and the respective approximate action-times of the indicated actions. The system may hypothesize that the indicated action may correspond to one of the encrypted actions having these action-times.

Abstract: Systems and methods for passive monitoring of computer communication that does not require performing any decryption. A monitoring system receives the traffic exchanged with each relevant application server, and identifies, in the traffic, sequences of messages—or “n-grams”—that appear to belong to a communication session between a pair of users. Subsequently, based on the numbers and types of identified n-grams, the system identifies each pair of users that are likely to be related to one another via the application, in that these users used the application to communicate (actively and/or passively) with one another. The system may identify those sequences of messages that, by virtue of the sizes of the messages in the sequence, and/or other properties of the messages that are readily discernable, indicate a possible user-pair relationship.

Abstract: A traffic-monitoring system that monitors encrypted traffic exchanged between IP addresses used by devices and a network, and further receives the user-action details that are passed over the network. By correlating between the times at which the encrypted traffic is exchanged and the times at which the user-action details are received, the system associates the user-action details with the IP addresses. In particular, for each action specified in the user-action details, the system identifies one or more IP addresses that may be the source of the action. Based on the IP addresses, the system may identify one or more users who may have performed the action. The system may correlate between the respective action-times of the encrypted actions and the respective approximate action-times of the indicated actions. The system may hypothesize that the indicated action may correspond to one of the encrypted actions having these action-times.

Abstract: Machine learning techniques for classifying encrypted traffic with a high degree of accuracy. The techniques do not require decrypting any traffic and may not require any manually-labeled traffic samples. An automated system uses an application of interest to perform a large number of user actions of various types. The system further records, in a log, the respective times at which the actions were performed. The system further receives the encrypted traffic exchanged between the system and the application server, and records properties of this traffic in a time series. Subsequently, by correlating between the times in the log and the times at which the traffic was received, the system matches each of the user actions with a corresponding portion of the traffic, which is assumed to have been generated by the user action. The system thus automatically builds a labeled training set, which may be used to train a network-traffic classifier.

Abstract: Systems and methods for passive monitoring of computer communication that does not require performing any decryption. A monitoring system receives the traffic exchanged with each relevant application server, and identifies, in the traffic, sequences of messages—or “n-grams”—that appear to belong to a communication session between a pair of users. Subsequently, based on the numbers and types of identified n-grams, the system identifies each pair of users that are likely to be related to one another via the application, in that these users used the application to communicate (actively and/or passively) with one another. The system may identify those sequences of messages that, by virtue of the sizes of the messages in the sequence, and/or other properties of the messages that are readily discernable, indicate a possible user-pair relationship.

Abstract: A method for maintaining the best available quality of a connection between to wireless-enabled devices in a local wireless network, comprises providing: (a) a link (session) tracking subsystem, for tracking the link (session) quality between PtP devices; and, (b) channel and route selection subsystem, for choosing the best channel and route between session peers; and switching to a different channel and/or route when a measured quality indicator triggers a change.

Abstract: A pull or push based caching management method that delivers retrieved content over a wireless data network. In its pull mode, distributed cache(s) and mapping modes are deployed, a request for content is received and classified to generate a quality of service (QoS) identifier. The QoS is attached with an identifier for requested content to a content mapping request message. The handling of the mapping request message is scheduled on the mapping node. The content mapping request message transmits to a selected mapping node. The requesting node receives a content mapping reply message. The content request is scheduled on the caching node and a content retrieval request message is transmitted from the requesting node to the one or more target caching nodes. A content retrieval reply message is received in return. In a push mode, instructions are provided for triggering a content retrieval operation and also QoS parameters.

Abstract: In a method for the dynamic content discovery in a distributed caching network the distribution of content popularity and its access frequency rate are used to determine the most appropriate mapping method(s) to use.