A device (110) arranged for wireless communication (130) according to a communication protocol has a processor (112) to execute a connection sequence according to a discovery protocol. The connection sequence comprises determining a current cluster identity and a current discovery window timing used by the device. Next, at least one other device (120,120′) within wireless range is detected, while further determining a detected cluster identity and a detected discovery window timing of the detected other device. Then it is detected whether the detected device is operating in a different cluster than the device by comparing the current cluster identity with the detected cluster identity or comparing the current discovery window timing with the detected discovery window timing. Finally, upon detecting said different cluster, a security process is executed, which may warn the user or abort the connection sequence. Thereby, a malicious device trying to manipulate the connection sequence is detected.

Embodiments for implementing intelligent risk detection and mitigation in a transport network by a processor. Data gathered from a plurality of data sources relating to an entity and a selected region of interest may be analyzed. Behavior of an entity, in relation to a risk event, may be learned and interpreted based on a plurality of identified contextual factors, geographical data, current data, historical data, a learned risk event model, or a combination thereof. One or more mitigation actions may be performed to mitigate risk of occurrence or a possible negative impact of the risk event caused at least in part by the behavior of the entity.

A method performed by one or more network node(s) of a wireless telecommunications network to dynamically manage encryption keys for multiple narrowband Internet of Things (NB-IoT) devices of the network. The network node(s) can maintain a database that stores a device profile for each of the NB-IoT devices and obtain multiple encryption keys for the multiple NB-IoT devices. The encryption keys are associated with different encryption strengths ranging from high to ultra-low encryption strengths. The network node(s) can allocate the encryption keys to the NB-IoT devices, detect a change in the condition of the network, capability or communications service of NB-IoT devices, and refresh the encryption keys accordingly to ensure that the network nodes properly balance encryption while providing efficient network performance.

A method of securing functionalities of an integrated subscriber identification module (iSIM) on an information handling system may include with an embedded controller (EC), detecting a powering-up process at the information handling system and determine a chain of trust access keys during bootup; with the execution of the EC, detecting and activating a wireless wide area network (WWAN) module; with the execution of the EC, detecting and accessing an integrated subscriber identity module (iSIM); with the execution of the EC, authenticating access to iSIM content including authorization information and carrier profile information with the chain of trust access keys generated from encryption keys based on digital signatures; and sending the authorization information and carrier profile information form the iSIM to the WWAN module for authentication, wirelessly, with a switched multimegabit data service (SM-DS) server associated with the carrier profile.

A method includes generating a decision matrix for selection of a particular Non-3GPP InterWorking Function (N3IWF) server among a plurality of N3IWF servers, the decision matrix having multiple criteria including multiple N3IWF capabilities. The method also includes dynamically weighting at least one of the multiple criteria in the decision matrix, based on one or more adjustment factors. The method further includes applying a multi-attribute decision making technique to the decision matrix to select the particular N3IWF server, among the plurality of N3IWF servers, for a mobile device to connect to in a 5G network.

A method includes generating a decision matrix for selection of a particular Non-3GPP InterWorking Function (N3IWF) server among a plurality of N3IWF servers, the decision matrix having multiple criteria including multiple N3IWF capabilities. The method also includes dynamically weighting at least one of the multiple criteria in the decision matrix, based on one or more adjustment factors. The method further includes applying a multi-attribute decision making technique to the decision matrix to select the particular N3IWF server, among the plurality of N3IWF servers, for a mobile device to connect to in a 5G network.

Aspects of the subject disclosure may include, for example, initializing a secure timer in a wireless device, determining whether a subscriber identification module (SIM) card installed in the wireless device comprises a carrier identity that matches a carrier identity stored in the machine-readable medium, establishing a network connection with a trusted server, starting the secure timer if the SIM card and network connection are satisfactory, periodically checking the network connection and SIM card until expiry of the secure timer, penalizing the secure timer responsive to a failure of the network connection or SIM card check, and responsive to expiry of the secure timer, unlocking a SIM lock. Other embodiments are disclosed.

Embodiments include methods for a network node to configure random access by one or more user equipment, UEs, in a cell of the wireless network. Such methods include providing (2040) one of the following to one or more UEs operating in the cell: an artificial intelligence/machine learning, AI/ML, predictive model that includes one or more input parameters and corresponding one or more output parameters that are associated with random-access configurations for the cell; or one or more random-access configurations for the cell, each random-access configuration associated with one or more values of output parameters of the AI/ML predictive model. Such methods include detecting (2090) a random access to the cell, by a particular UE, according to a particular random-access configuration associated with particular values of the output parameters. Other embodiments include complementary methods for a UE, as well as network nodes and UEs configured to perform such methods.

Methods, systems, and apparatus, for handling applications in an ambient computing system with a privacy processor. One of the methods includes to remain in a monitoring power state until a controller receives an interrupt indicating that one or more sensor signals are present. The one or more sensor signals are provided as input to a machine learning engine. An inference pass is performed by the machine learning engine to generate an output representing a particular context that is specific to a particular user. It is determined that one or more components of an ambient computing system should be disabled based on the on the particular context for the particular user. In response, the one or more components of the ambient computing system are disabled.

A distributed system provides access by a principal to a resource associated with sensitive data. Micro-services in communication with an authorization engine each include a resource provider that receives a resource action request from the principal to access the resource, determines a context for the request, and transmits the context to the authorization engine in an authorization request. The authorization engine receives the authorization request, resolves the authorization request context against a plurality of pre-defined resource conditions, and responds to the resource provider with an authorization response of allow, deny, or allow-with-conditions. The context for the request includes metadata regarding attributes of the principal, and each of the resource conditions includes a logical expression operating upon the attributes.