Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: A system described herein may identify a particular value that was wirelessly received by a User Equipment (“UE”) at a first time. The particular value may have been recorded to a blockchain, along with a second time, at which the particular value was outputted by a base station of a wireless network. The particular value may include a randomly generated number. The system may determine a location of the base station from which the particular value was outputted. The location of the UE may be determined based on a delay time associated with the particular value, which may be determined based on a difference between the first and second times. The location of the UE may further be determined (e.g., using triangulation techniques) based on values outputted by other base stations, for which the blockchain includes records indicating times at which such values were outputted by the other base stations.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: One or more computing devices, systems, and/or methods for detecting spoofing attacks are provided. Location information of a base station may be evaluated to determine a true position of the base station. Satellite signals received by the base station may be processed and evaluated to calculate a real time position of the base station. A distance between the real time position and the true position may be calculated. In response to the distance exceeding a threshold distance, an alert is generated to indicate that the base station is experiencing a spoofing attack.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: A device determines, based on location verification data that has been received, that the device is indoors at a first geographic location. The device determines a base measured barometric pressure, and an initial floor that the device is located on in a structure that includes the first geographic location. The device determines an adjusted measured barometric pressure for a second geographic location based on a second measured barometric pressure for the second geographic location and one or more reference barometric pressures that are associated with a reference location. The device determines an altitude for the second geographic location based on the base measured barometric pressure and the adjusted measured barometric pressures. The device causes a server to predict a floor that the device is located on at the second geographic location and to provide floor data that identifies the floor to an interface that is accessible to the device.

Abstract: A microservice node can receive a request for information identifying a corrected physical location of a client device. The request can include raw satellite data associated with the client device. The microservice node can convert the raw satellite data to a Radio Technical Commission for Maritime Services (RTCM) format. The microservice node can determine, based on converting the raw satellite data to the RTCM format, an estimated physical location of the client device. The microservice node can receive, based on transmitting a request to a network real-time kinematics (RTK) device, corrections data associated with the estimated physical location of the client device. The microservice node can determine, using a cloud RTK engine, the corrected physical location of the client device based on the estimated physical location and corrections data. The microservice node can transmit, to the client device, the information identifying the corrected physical location of the client device.

Abstract: A microservice node can receive a request for information identifying a corrected physical location of a client device. The request can include raw satellite data associated with the client device. The microservice node can convert the raw satellite data to a Radio Technical Commission for Maritime Services (RTCM) format. The microservice node can determine, based on converting the raw satellite data to the RTCM format, an estimated physical location of the client device. The microservice node can receive, based on transmitting a request to a network real-time kinematics (RTK) device, corrections data associated with the estimated physical location of the client device. The microservice node can determine, using a cloud RTK engine, the corrected physical location of the client device based on the estimated physical location and corrections data. The microservice node can transmit, to the client device, the information identifying the corrected physical location of the client device.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: A device determines, based on location verification data that has been received, that the device is indoors at a first geographic location. The device determines a base measured barometric pressure, and an initial floor that the device is located on in a structure that includes the first geographic location. The device determines an adjusted measured barometric pressure for a second geographic location based on a second measured barometric pressure for the second geographic location and one or more reference barometric pressures that are associated with a reference location. The device determines an altitude for the second geographic location based on the base measured barometric pressure and the adjusted measured barometric pressures. The device causes a server to predict a floor that the device is located on at the second geographic location and to provide floor data that identifies the floor to an interface that is accessible to the device.

Abstract: A device determines, based on location verification data that has been received, that the device is indoors at a first geographic location. The device determines a base measured barometric pressure, and an initial floor that the device is located on in a structure that includes the first geographic location. The device determines an adjusted measured barometric pressure for a second geographic location based on a second measured barometric pressure for the second geographic location and one or more reference barometric pressures that are associated with a reference location. The device determines an altitude for the second geographic location based on the base measured barometric pressure and the adjusted measured barometric pressures. The device causes a server to predict a floor that the device is located on at the second geographic location and to provide floor data that identifies the floor to an interface that is accessible to the device.

Abstract: A microservice node can receive a request for information identifying a corrected physical location of a client device. The request can include raw satellite data associated with the client device. The microservice node can convert the raw satellite data to a Radio Technical Commission for Maritime Services (RTCM) format. The microservice node can determine, based on converting the raw satellite data to the RTCM format, an estimated physical location of the client device. The microservice node can receive, based on transmitting a request to a network real-time kinematics (RTK) device, corrections data associated with the estimated physical location of the client device. The microservice node can determine, using a cloud RTK engine, the corrected physical location of the client device based on the estimated physical location and corrections data. The microservice node can transmit, to the client device, the information identifying the corrected physical location of the client device.