Embodiments of the disclosure provide a sensitivity enhancing radio frequency identification technique using machine learning. A method according to the disclosure includes obtaining an input signal associated with a radio frequency (RF) transmission; separately extracting spatial domain features, time-frequency domain features, and temporal domain features from the input signal; processing the spatial domain features, time-frequency domain features, and temporal domain features to generate an attentional vector; and predicting at least one descriptor for an emitter of the RF transmission based on the attentional vector.

Embodiments of the disclosure provide a sensitivity enhancing radio frequency identification technique using machine learning. A method according to the disclosure includes obtaining an input signal associated with a radio frequency (RF) transmission; separately extracting spatial domain features, time-frequency domain features, and temporal domain features from the input signal; processing the spatial domain features, time-frequency domain features, and temporal domain features to generate an attentional vector; and predicting at least one descriptor for an emitter of the RF transmission based on the attentional vector.

Systems and methods for detecting a rogue network device at a physical layer include obtaining physical layer characteristics of a link between a first network device and a second network device; analyzing the physical layer characteristics of the link; and detecting the rogue network device based on the analyzed physical layer characteristics, wherein the rogue network device was inserted in the link and causes detectable variances in the physical layer characteristics. The physical layer characteristics can include one of noise introduced in clock frequency and jitter.

Systems and methods for detecting a rogue network device at a physical layer include obtaining physical layer characteristics of a link between a first network device and a second network device; analyzing the physical layer characteristics of the link; and detecting the rogue network device based on the analyzed physical layer characteristics, wherein the rogue network device was inserted in the link and causes detectable variances in the physical layer characteristics. The physical layer characteristics can include one of noise introduced in clock frequency and jitter.

A system receives, from a user equipment, a request for access to a wireless communication network and identifies, based on the request, baseline radio frequency (RF) response information associated with the user equipment. The system obtains dynamic RF response information associated with the user equipment and authenticates, based on the dynamic RF response information and the baseline RF response information, the user equipment. The system thereby grants the user equipment access to the wireless communication network and determines context information associated with the user equipment. The system generates, based on the baseline RF response information and the context information, user equipment profile information and sends, to a base station associated with the wireless communication network, the user equipment profile information to facilitate access to the wireless communication network by the user equipment.

A system receives, from a user equipment, a request for access to a wireless communication network and identifies, based on the request, baseline radio frequency (RF) response information associated with the user equipment. The system obtains dynamic RF response information associated with the user equipment and authenticates, based on the dynamic RF response information and the baseline RF response information, the user equipment. The system thereby grants the user equipment access to the wireless communication network and determines context information associated with the user equipment. The system generates, based on the baseline RF response information and the context information, user equipment profile information and sends, to a base station associated with the wireless communication network, the user equipment profile information to facilitate access to the wireless communication network by the user equipment.

In some implementations, a system may obtain dynamic radio frequency (RF) response information associated with a user equipment and additional dynamic information associated with the user equipment. The system may determine, based on the dynamic RF response information and the additional dynamic information, a current behavior profile of the user equipment. The system may compare the current behavior profile and a baseline behavior profile of the user equipment to generate comparison information. The system may grant or deny, based on the comparison information, the user equipment access to a resource of a private network.

A method for controlling the authentication of a user by an authentication device, the authentication being implemented by verification, by the authentication device, that a code entered by the user on an interaction interface of the authentication device corresponds to a predetermined code associated with the user is disclosed. A signal representative of a radio signal characteristic of an interaction of the user on the interaction interface of the authentication device is received by the user's terminal. The radio signal is transmitted by an antenna of the authentication device via a channel using electromagnetic wave conduction capacities of the user's body when the user enters the code. It is verified that the radio signal corresponds to a previously stored reference signal, and, in the event of a positive verification, a piece of data representative of the user's identity is transmitted to the authentication device for verification with the user's identity.

This invention provides a device fingerprint extraction method based on smart phone sensor, which comprises the following steps: Step I: Data acquisition; Step II: Data preprocessing; Step III: ADC value recovery; Step IV: Gain matrix estimation; Step V: Validity check; Step VI: Device fingerprint ID generation. This invention can accurately extract the device fingerprint and can uniquely identify device when the device fingerprint ID doesn't change when certain devices are sampled for ex-factory setting recovery, machine upgrade and time point testing.

A converged radio unit (RU) configured for semantic-less retransmissions comprises a radio-frequency machine learning (RFML) radio unit (RFML RU) and a radio-access network (RAN) radio unit (RU). The RFML RU may be configured to receive an RF signal comprising a packet, decode a PHY layer preamble of the packet, and apply a machine-learning (ML) model to information obtained from only the PHY layer preamble to classify the packet. When the packet is classified be a tactical data link (TDL) packet based on the information obtained only from the PHY layer preamble, the RFML RU may retransmit the packet. When the packet is classified a RAN protocol packet, the RFML RU may be configured to signal the RAN RU to process the RAN protocol packet in accordance with a RAN protocol.