Abstract: Aspects of the subject disclosure may include, for example, obtaining a group of threat research reports, obtaining a group of indicators of compromise (IOCs), and monitoring a network of Internet of Things (IoT) devices. Further embodiments can include determining a group of possible malware on the network based on the monitoring of the network and generating a network security recommendation based on the group of threat research reports, the group of IOCs, and the group of possible malware. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving application traffic from a plurality of Internet of Things (IoT) devices processing an application, including receiving the application traffic over a mobile communication network, clustering the application traffic to identify clusters of similar IoT devices based on common characteristics of the application traffic, receiving unknown traffic over the mobile communication network, determining a closest cluster based on similarities between the unknown traffic and respective clusters of the clusters of IoT devices, decomposing traffic signals of the closest cluster into trend, period, and noise information, forming decomposed traffic, reconstructing the decomposed traffic into reconstructed traffic based on the trend information and the period information to remove noise information from the traffic signals of the closest cluster, forming a baseline for the closest cluster, detecting an anomaly in the reconstructed traffic based on a variat

Abstract: Aspects of the subject disclosure may include, for example, training a large language model (LLM) on data related to malware that may infect internet of things (IoT) devices communicating on a mobility network, receiving, from the LLM, information about malware affecting the IoT devices, conducting a dialog with the LLM to develop additional information about the malware affecting the IoT devices, wherein the conducting the dialog with the LLM comprises providing textual questions to the LLM and receiving textual answers to the textual questions from the LLM, and providing, to a device operator associated with the IoT devices, diagnostic information about a malware threat to IoT devices in the network and prescriptive information that may be used to avoid the malware threat, wherein the diagnostic information and the prescriptive information are based on the dialog with the LLM. Other embodiments are disclosed.

Abstract: A robocall mitigation system can receive a call detail record (“CDR”) associated with a voice call conducted over a mobile telecommunications network. The robocall mitigation system can determine, from the CDR, an originating telephone number and a destination telephone number for the voice call. The robocall mitigation system can determine that the destination telephone number is associated with a connected vehicle. The robocall mitigation system can compare the originating telephone number to a set of authorized originating telephone numbers. The robocall mitigation system can determine, based upon comparing the originating telephone number to the set of authorized originating telephone numbers, that the originating telephone number was unauthorized. The robocall mitigation system can generate a robocall alert record. The robocall mitigation system can output the robocall alert record.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a group of threat research reports, obtaining a group of indicators of compromise (IOCs), and monitoring a network of Internet of Things (IoT) devices. Further embodiments can include determining a group of possible malware on the network based on the monitoring of the network, and generating a network security recommendation based on the group of threat research reports based on the group of threat research reports, the group of IOCs, and the group of possible malware. Other embodiments are disclosed.