Abstract: The concepts and technologies disclosed herein are directed detecting and mitigating drive-by home WI-FI hijack attacks. According to one aspect, a war driving activity correlation system can obtain a report from a home gateway system. The report can identify a suspicious device attempting to connect to a WI-FI network provided, at least in part, by the home gateway system. The war driving activity correlation system can determine, based upon the report, a route of an attacker who uses the suspicious device. The war driving activity correlation system can send instructions to the home gateway system. The instructions can include the route and can specify one or more mitigation actions to be performed by the home gateway system, such as blacklisting a media access control address associated with the suspicious device. The mitigation action(s) can be based upon one or more policies.

Abstract: Aspects of detecting and mitigating denial of service (“DoS”) attacks over home gateway network address translation (“NAT”) are disclosed herein. According to one aspect disclosed herein, a home gateway system can detect that a NAT table is overpopulated. In response to detecting that the NAT table is overpopulated, the home gateway system can determine a mitigation action to be performed. The home gateway system can then perform the mitigation action in an attempt to mitigate an effect of the NAT table overpopulation.

Abstract: A distributed denial of service (“DDoS”) attack profiler can determine a plurality of DDoS attack properties associated with a DDoS attack that utilizes an Internet of Things (“IoT”) device operating in communication with a home gateway. The DDoS attack profiler can create a DDoS attack profile and can provide a DDoS attack report based upon the DDoS attack profile to a correlator. An IoT device profiler can determine a plurality of IoT device properties and can create, based upon the plurality of IoT device properties, an IoT device profile. The IoT device profiler can create an anomaly report that identifies an anomaly associated with the IoT device. The correlator can correlate the DDoS attack report with the anomaly report to determine if a match exists. In response to determining that the match exists, the home gateway system can store the bot match record in a bot match repository.

Abstract: The concepts and technologies disclosed herein are directed to home gateway monitoring for vulnerable home Internet of Things (“IoT”) devices. According to one aspect disclosed herein, a home gateway can scan a home network address space of a home network for an IoT device. The home gateway can perform a vulnerability test on the IoT device to determine whether the IoT device is vulnerable to a known vulnerability. In response to determining that the IoT device is vulnerable to the known vulnerability, the home gateway can change a device status of the IoT device to a vulnerable status and can change a permissions level of the IoT device to a quarantine permissions level.

Abstract: An access point impersonation protection system (“the system”) can scan for wireless network signals to detect a wireless network provided by a malicious access point. The system can collect a network feature associated with the wireless network. The system can analyze the network feature and can provide analysis results to a machine learning classifier. The machine learning classifier can assign a classification to the access point. The classification can be a benign classification indicative of the access point being benign. The classification can be a malicious classification indicative of the access point being malicious. The network feature can be an active time, an SSID name, a vendor, a model, a signal strength, an authentication requirement, or a combination thereof. The system can alert upon identifying a malicious access point and apply counter measures to prevent the malicious access point from causing harm to nearby devices.

Abstract: The described technology is generally directed towards beam recovery for an antenna array. One or more recovery beams or recovery beam patterns can be defined for an antenna array, and in response to a failure, the antenna array can be restored to a defined recovery beam or recovery beam pattern. Techniques for defining recovery beams and recovery beam patterns for the antenna array, selecting a recovery beam or recovery beam pattern for the antenna array, and entering a selected recovery beam or recovery beam pattern by the antenna array are also disclosed.

Abstract: The described technology is generally directed towards beam recovery for an antenna array. One or more recovery beams or recovery beam patterns can be defined for an antenna array, and in response to a failure, the antenna array can be restored to a defined recovery beam or recovery beam pattern. Techniques for defining recovery beams and recovery beam patterns for the antenna array, selecting a recovery beam or recovery beam pattern for the antenna array, and entering a selected recovery beam or recovery beam pattern by the antenna array are also disclosed.

Abstract: Deployment of Internet-of-things devices can comprise sensors deployed in remote and hard to reach areas and locations. Due to lack of access to reliable power, these sensors cannot always be connected to a network and also have limited computation power. Consequently, a mechanism can be established to periodically access these sensors and collect data from them. The mechanism can utilize a mobile radio unit device to serve as data collectors. The mobile radio unit device can make use of an adaptive beam scanning to perform sensory data collection via the beam scanning operation. Additionally, the platform can also comprise a radio access network intelligent controller to manage the data collecting radio units by providing specific instructions and data collection methodologies.

Abstract: Given real-time user equipment (UE) measurements from an open radio access network (O-RAN) infrastructure, a radio access network intelligent controller (RIC) can compute a UE distribution context space. The O-RAN can comprise gNode-Bs, centralized units, and distributed units. The UE distribution context space computations can be performed by a UE context correlator module of the RIC. The UE context correlator module can also utilize a pre-defined UE context model, which contains definitions and values for various UE context attributes to generate adaptive beam-forming patterns.

Abstract: An intelligent determination of a set of candidate handover-beams for a radio access network (RAN) can be facilitated by a RAN intelligent controller (RIC). The RIC can obtain data associated with user equipment devices and their current state, historical patterns, and a current state of serving network node devices and neighboring network node devices. Based on the obtained data, a handover procedure can be optimized by the user equipment by estimating the signal quality from a source cell and neighboring cells. Additionally, the user equipment can send measurement reports to the network to identify and quantify the quality of reference signals transmitted by the network node devices.

Abstract: Precoding matrix computations for a large number of antenna arrays can be used to generate efficiencies within a wireless network. Utilizing network topology and context data in conjunction with known available network resources and mobile device measurements can facilitate gains in power and spectral efficiency and reduction in computation complexity posed by current procedures. To take advantage of multiple paths, the precoding matrix can be known at the radio units for each mobile device.

Abstract: An example method includes estimating a number of people expected to attend a non-ticketed event, based on electronic data including social media postings, estimating a per-person amount of network traffic expected to be generated on a communications network during the non-ticketed event, based at least in part on historical per-person network traffic statistics for a historical event of a similar type to the non-ticketed event, calculating an amount of total network traffic expected to be generated during the non-ticketed event, based at least on the number of people expected to attend the non-ticketed event and the per-person amount of network traffic, and implementing a modification to an infrastructure of the communications network in a geographic location of the non-ticketed event, based at least in part on the amount of total network traffic expected to be generated during the non-ticketed event.

Abstract: An example method includes estimating a number of people expected to attend a non-ticketed event, based on electronic data including social media postings, estimating a per-person amount of network traffic expected to be generated on a communications network during the non-ticketed event, based at least in part on historical per-person network traffic statistics for a historical event of a similar type to the non-ticketed event, calculating an amount of total network traffic expected to be generated during the non-ticketed event, based at least on the number of people expected to attend the non-ticketed event and the per-person amount of network traffic, and implementing a modification to an infrastructure of the communications network in a geographic location of the non-ticketed event, based at least in part on the amount of total network traffic expected to be generated during the non-ticketed event.

Abstract: Precoding matrix computations for a large number of antenna arrays can be used to generate efficiencies within a wireless network. Utilizing network topology and context data in conjunction with known available network resources and mobile device measurements can facilitate gains in power and spectral efficiency and reduction in computation complexity posed by current procedures. To take advantage of multiple paths, the precoding matrix can be known at the radio units for each mobile device.

Abstract: The described technology is generally directed towards network slice management. A mobile communications network can comprise multiple sub-networks, namely, as an access network, a transport network, and a core network. Access network resources can be managed according to the techniques provided herein to meet service level agreement (SLA) commitments associated with network slices. Furthermore, resources of any sub-network can be managed in a manner that accounts for constraints imposed by the other sub-networks.

Abstract: A radio access network intelligent controller (RIC) platform can enable various highly secure, reliable, fast communications for first responders during emergency situations. Self-organizing service chaining of public safety edge applications, can be enabled in both time and space when triggered by an emergency situation. The RIC platform can perform reassignment of resources and network slices according to historical data and situational analysis. The RIC platform can select and provide the best frequencies and resources to ensure that first responders have communication services that do not get affected by anomalies such as network load, congestion, and/or related degradations.

Abstract: Given real-time user equipment (UE) measurements from an open radio access network (O-RAN) infrastructure, a radio access network intelligent controller (RIC) can compute a UE distribution context space. The O-RAN can comprise gNode-Bs, centralized units, and distributed units. The UE distribution context space computations can be performed by a UE context correlator module of the RIC. The UE context correlator module can also utilize a pre-defined UE context model, which contains definitions and values for various UE context attributes to generate adaptive beam-forming patterns.

Abstract: The described technology is generally directed towards an intent design tool for communication networks. A graphical user interface provided by the intent design tool can include a collection of communication network topology elements and a communication network intent topology design area. Selected communication network topology elements can be placed into the communication network intent topology design area, and connection types between the selected communication network topology elements can also be specified in the communication network intent topology design area, in order to define custom communication network intent topologies.

Abstract: A radio access network intelligent controller (RIC) platform can enable various highly secure, reliable, fast communications for first responders during emergency situations. The RIC can utilize radio access network (RAN) resources, for example, beam management procedures to facilitate the use of virtual zones. The virtual zones can be generated dynamically by leveraging collaborating mobile devices from various zones. The virtual zones can be distributed entities that span across real-physical zones but are managed as a single logical entity under a common set of policies. The creation and management of virtual zones can be performed by a group of corresponding RICs as a distributed system.

Abstract: The described technology is generally directed towards network slice management. A mobile communications network can comprise multiple sub-networks, namely, as an access network, a transport network, and a core network. Access network resources can be managed according to the techniques provided herein to meet service level agreement (SLA) commitments associated with network slices. Furthermore, resources of any sub-network can be managed in a manner that accounts for constraints imposed by the other sub-networks.