Abstract: A method for detecting threat pathways using sequence graphs includes constructing a sequence graph from a set of data containing information about activities in a telecommunications service provider network, where the sequence graph represents a subset of the activities that occurs as a sequence, providing an embedding of the sequence graph as input to a machine learning model, wherein the machine learning model has been trained to detect when an input embedding of a sequence graph is likely to indicate a threat activity, determining, based on an output of the machine learning model, whether the subset of the activities is indicative of the threat activity, and initiating a remedial action to mitigate the threat activity.

Abstract: Aspects of the subject disclosure may include, for example, monitoring a first activity undertaken by a communication device during a first communication session, generating, based on the monitoring, first data that indicates an amount of time that is spent on the first activity, comparing, based on the generating, the first data to a threshold, and identifying, based on at least the comparing, an action to take when the amount of time that is spent on the first activity exceeds the threshold. Other embodiments are disclosed.

Abstract: A method for detecting threat pathways using sequence graphs includes constructing a sequence graph from a set of data containing information about activities in a telecommunications service provider network, where the sequence graph represents a subset of the activities that occurs as a sequence, providing an embedding of the sequence graph as input to a machine learning model, wherein the machine learning model has been trained to detect when an input embedding of a sequence graph is likely to indicate a threat activity, determining, based on an output of the machine learning model, whether the subset of the activities is indicative of the threat activity, and initiating a remedial action to mitigate the threat activity.

Abstract: Aspects of the subject disclosure may include, for example, applying a topic detection process to documents to obtain automatically detected topics and groups of automatically detected words, comparing the automatically detected topics with manually determined topics to determine actual purity metrics, determining an error metric based on a measure of deviation between ideal purity metrics and the actual purity metrics, and adjusting a parameter of the topic detection process according to the error metric resulting in an adjusted topic detection process. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, applying a topic detection process to documents to obtain automatically detected topics and groups of automatically detected words, comparing the automatically detected topics with manually determined topics to determine actual purity metrics, determining an error metric based on a measure of deviation between ideal purity metrics and the actual purity metrics, and adjusting a parameter of the topic detection process according to the error metric resulting in an adjusted topic detection process. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, applying a topic detection process to documents to obtain automatically detected topics and groups of automatically detected words, comparing the automatically detected topics with manually determined topics to determine actual purity metrics, determining an error metric based on a measure of deviation between ideal purity metrics and the actual purity metrics, and adjusting a parameter of the topic detection process according to the error metric resulting in an adjusted topic detection process. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, applying a topic detection process to documents to obtain automatically detected topics and groups of automatically detected words, comparing the automatically detected topics with manually determined topics to determine actual purity metrics, determining an error metric based on a measure of deviation between ideal purity metrics and the actual purity metrics, and adjusting a parameter of the topic detection process according to the error metric resulting in an adjusted topic detection process. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, applying a topic detection process to documents to obtain automatically detected topics and groups of automatically detected words, comparing the automatically detected topics with manually determined topics to determine actual purity metrics, determining an error metric based on a measure of deviation between ideal purity metrics and the actual purity metrics, and adjusting a parameter of the topic detection process according to the error metric resulting in an adjusted topic detection process. Other embodiments are disclosed.