Abstract: Various embodiments for resolving customer communication security vulnerabilities are provided. Customer traffic data is stored in a database and analyzed to identify problem traffic. A report of a first user device and a usage history for the first user device is obtained. Similarities between the usage history of the first user device and the problem traffic are searched for to identify an issue. A first vulnerability is remedied on the first user device by a first remote action in response to the issue being identified. A second user device that is in a same account as the first user device and that has engaged in similar problematic communications as the first user device is identified. A second vulnerability is proactively remedied on the second user device by a second remote action.

Abstract: A system for malware and anomaly detection via activity recognition based on sensor is disclosed. The system may analyze sensor data collected during a selected time period from one or more sensors that are associated with a device. Once the sensor data is analyzed, the system may determine a context of the device when the device is in a connected state. The system may determine the context of the device based on the sensor data collected during the selected time period. The system may also determine if traffic received or transmitted by the device during the connected state is in a white list. Furthermore, the system may transmit an alert if the traffic is determined to not be in the white list or if the context determined for the device indicates that the context does not correlate with the traffic.

Abstract: Various embodiments for resolving customer communication security vulnerabilities are provided. Customer traffic data is stored in a database and analyzed to identify problem traffic. A report of a first user device and a usage history for the first user device is obtained. Similarities between the usage history of the first user device and the problem traffic are searched for to identify an issue. A first vulnerability is remedied on the first user device by a first remote action in response to the issue being identified. A second user device that is in a same account as the first user device and that has engaged in similar problematic communications as the first user device is identified. A second vulnerability is proactively remedied on the second user device by a second remote action.

Abstract: Customer communication security vulnerabilities are resolved. A usage history is obtained for a user device including communications involving the user device. Pattern recognition is applied to the usage history. The user device is assigned with a risk classification from a predetermined set of possible risk classifications, based on the pattern recognition. A vulnerability on the user device is remedied when the risk classification exceeds a predetermined threshold.

Abstract: A request is received over a network to resolve a problem relating to a networked user device. The request is accepted in order to provide user service. Based on the request, one of multiple available diagnostic algorithms is selected to analyze user data related to a user's account to identify symptoms of the problem and diagnose a cause of the symptoms identified.

Abstract: A method and apparatus for identifying a potential source of SMS spam are disclosed. For example, the method collects a plurality of call detail records, extracts at least one feature from each of the plurality of call detail records, and identifies the potential source of the short message service spam by analyzing the at least one feature that is extracted from each of the plurality of call detail records.

Abstract: A request is received over a network to resolve a problem relating to a networked user device. The request is accepted in order to provide user service. Based on the request, one of multiple available diagnostic algorithms is selected to analyze user data related to a user's account to identify symptoms of the problem and diagnose a cause of the symptoms identified.

Abstract: A method that incorporates teachings of the subject disclosure may include, for example, obtaining data relating to a set of collision events on a shared channel on a wireless network according to a contention-based access protocol in which a plurality of terminals attempt to access the channel contemporaneously. A probability of collision in the channel is estimated and a probability distribution of time intervals between access attempts is generated based on the estimated probability of collision. Empirical and theoretical cumulative distribution functions for the time intervals are calculated, and compared to identify a malfunctioning terminal not operating in accordance with the protocol. Other embodiments are disclosed.

Abstract: A system for malware and anomaly detection via activity recognition based on sensor is disclosed. The system may analyze sensor data collected during a selected time period from one or more sensors that are associated with a device. Once the sensor data is analyzed, the system may determine a context of the device when the device is in a connected state. The system may determine the context of the device based on the sensor data collected during the selected time period. The system may also determine if traffic received or transmitted by the device during the connected state is in a white list. Furthermore, the system may transmit an alert if the traffic is determined to not be in the white list or if the context determined for the device indicates that the context does not correlate with the traffic.

Abstract: A method, non-transitory computer readable medium and apparatus for deriving trustful metadata for an application are disclosed. For example, the method crawls online for the application, analyzes the application to determine a function of the application, and generates trustful meta-data for the application based upon the function of the application.

Abstract: A request is received over a network to resolve a problem relating to a networked user device. The request is accepted in order to provide user service. Based on the request, one of multiple available diagnostic algorithms is selected to analyze user data related to a user's account to identify symptoms of the problem and diagnose a cause of the symptoms identified.

Abstract: A system for malware and anomaly detection via activity recognition based on sensor is disclosed. The system may analyze sensor data collected during a selected time period from one or more sensors that are associated with a device. Once the sensor data is analyzed, the system may determine a context of the device when the device is in a connected state. The system may determine the context of the device based on the sensor data collected during the selected time period. The system may also determine if traffic received or transmitted by the device during the connected state is in a white list. Furthermore, the system may transmit an alert if the traffic is determined to not be in the white list or if the context determined for the device indicates that the context does not correlate with the traffic.

Abstract: A method, non-transitory computer readable medium and apparatus for deriving trustful metadata for an application are disclosed. For example, the method crawls online for the application, analyzes the application to determine a function of the application, and generates trustful meta-data for the application based upon the function of the application.

Abstract: A method and apparatus for identifying a potential source of SMS spam are disclosed. For example, the method collects a plurality of call detail records, extracts at least one feature from each of the plurality of call detail records, and identifies the potential source of the short message service spam by analyzing the at least one feature that is extracted from each of the plurality of call detail records.

Abstract: A method, non-transitory computer readable medium and apparatus for deriving trustful metadata for an application are disclosed. For example, the method crawls online for the application, analyzes the application to determine a function of the application, and generates trustful meta-data for the application based upon the function of the application.

Abstract: A method and apparatus for identifying a potential source of SMS spam are disclosed. For example, the method collects a plurality of call detail records, extracts at least one feature from each of the plurality of call detail records, and identifies the potential source of the short message service spam by analyzing the at least one feature that is extracted from each of the plurality of call detail records.

Abstract: A method that incorporates teachings of the subject disclosure may include, for example, obtaining data relating to a set of collision events on a shared channel on a wireless network according to a contention-based access protocol in which a plurality of terminals attempt to access the channel contemporaneously. A probability of collision in the channel is estimated and a probability distribution of time intervals between access attempts is generated based on the estimated probability of collision. Empirical and theoretical cumulative distribution functions for the time intervals are calculated, and compared to identify a malfunctioning terminal not operating in accordance with the protocol. Other embodiments are disclosed.

Abstract: A system for malware and anomaly detection via activity recognition based on sensor is disclosed. The system may analyze sensor data collected during a selected time period from one or more sensors that are associated with a device. Once the sensor data is analyzed, the system may determine a context of the device when the device is in a connected state. The system may determine the context of the device based on the sensor data collected during the selected time period. The system may also determine if traffic received or transmitted by the device during the connected state is in a white list. Furthermore, the system may transmit an alert if the traffic is determined to not be in the white list or if the context determined for the device indicates that the context does not correlate with the traffic.

Abstract: Customer communication security vulnerabilities are resolved. A usage history is obtained for a user device including communications involving the user device. Pattern recognition is applied to the usage history. The user device is assigned with a risk classification from a predetermined set of possible risk classifications, based on the pattern recognition. A vulnerability on the user device is remedied when the risk classification exceeds a predetermined threshold.

Abstract: A system for malware and anomaly detection via activity recognition based on sensor is disclosed. The system may analyze sensor data collected during a selected time period from one or more sensors that are associated with a device. Once the sensor data is analyzed, the system may determine a context of the device when the device is in a connected state. The system may determine the context of the device based on the sensor data collected during the selected time period. The system may also determine if traffic received or transmitted by the device during the connected state is in a white list. Furthermore, the system may transmit an alert if the traffic is determined to not be in the white list or if the context determined for the device indicates that the context does not correlate with the traffic.