Abstract: An autonomous intelligent agent operates in a distributed computing environment to analyze data collected from a data pool, wherein the data collection is performed in a manner that is independent of activities of a deep-learning neural network (DNN) that fetches data from the data pool. A computer processor circuit implementing the agent comprises at least one generative adversarial neural network (GNN) and at least one Stochastic Neural Network (SNN). The agent retrieves original data from a data pool; uses the SNN to add noise to multiple evaluations of the original data; from the multiple evaluations, determines a proximity of the original data to a decision boundary; based on the proximity, determines if the original data is adversarial; upon determining that the original data is adversarial, employs the GNN to fabricate benign data from the original data; and then replaces the original data in the data pool with the benign data.

Abstract: A computer-implementable method employs network signal metadata to train a cognitive learning and inference system to produce an inferred function, wherein the metadata comprises a syntactic structure of at least one network communication protocol. The inferred function is used to map metadata of a detected network signal to a cognitive profile of a transmitter of the detected network signal. The mapping effects intelligent discrimination of the transmitter from at least one other transmitter through corroborative or negating evidentiary observation of properties associated with the metadata of the detected network signal. Information content in the network signal can be determined from the inferred function or the cognitive profile without demodulating the network signal.

Abstract: A computer-implementable method employs network signal metadata to train a cognitive learning and inference system to produce an inferred function, wherein the metadata comprises a syntactic structure of at least one network communication protocol. The inferred function is used to map metadata of a detected network signal to a cognitive profile of a transmitter of the detected network signal. The mapping effects intelligent discrimination of the transmitter from at least one other transmitter through corroborative or negating evidentiary observation of properties associated with the metadata of the detected network signal. Information content in the network signal can be determined from the inferred function or the cognitive profile without demodulating the network signal.

Abstract: A computer-implementable method employs radio signal metadata to train a cognitive learning and inference system to produce an inferred function, wherein the metadata comprises a syntactic structure of at least one radio communication protocol. The inferred function is used to map metadata of a detected radio signal to a cognitive profile of a transmitter of the detected radio signal. The mapping effects intelligent discrimination of the transmitter from at least one other transmitter through corroborative or negating evidentiary observation of properties associated with the metadata of the detected radio signal. A response to the transmitter is based upon the mapping.

Abstract: A radio transmitter adjusts its radio frequency (RF) fingerprint to defeat RF fingerprinting identification without destroying the content of its transmissions. The radio transmitter comprises a frequency-upconverter configured to upconvert a baseband or intermediate-frequency signal to an RF signal, and an amplifier to amplify the RF signal to produce a transmission signal. An RF fingerprint control circuit changes the non-linear behavior of the frequency-upconverter or the amplifier in order to change the RF fingerprint. The transmitter may create RF fingerprint “personalities” to be paired with different radio protocol behaviors and subscriber terminal identification codes (e.g., MAC addresses or SMSIs) for generating different radio identities.

Abstract: In a wireless communication system, a secure communication link is provided by producing a set of reference symbols selected from a modulation symbol constellation; generating a data-bearing pre-coding transform from information to be transmitted to a receiver; applying the data-bearing pre-coding transform to the set of reference symbols, thereby distorting the reference symbols with respect to the information, to produce a linear transformation signal; and transmitting the linear transformation signal to the receiver. The reference symbols are known at the receiver. The receiver removes the reference symbols from the linear transformation signal and decodes the data-bearing pre-coding transform.

Abstract: A computer-implementable method employs radio signal metadata to train a cognitive learning and inference system to produce an inferred function, wherein the metadata comprises a syntactic structure of at least one radio communication protocol. The inferred function is used to map metadata of a detected radio signal to a cognitive profile of a transmitter of the detected radio signal. The mapping effects intelligent discrimination of the transmitter from at least one other transmitter through corroborative or negating evidentiary observation of properties associated with the metadata of the detected radio signal. A response to the transmitter is based upon the mapping.

Abstract: A computer-implementable method for generating a cognitive insight is performed by a counter-unmanned autonomous vehicle (UAV) system. The method comprises receiving training data based upon sensor measurements of at least one UAV for processing in a cognitive learning and inference system. The system performs a plurality of machine learning operations on the training data to generate a cognitive profile of the at least one UAV. A cognitive insight is generated based upon the cognitive profile, and a countermeasure is enacted against the UAV based upon the cognitive insight.

Abstract: In a wireless communication system, a secure communication link is provided by producing a set of reference symbols selected from a modulation symbol constellation; generating a data-bearing pre-coding transform from information to be transmitted to a receiver; applying the data-bearing pre-coding transform to the set of reference symbols, thereby distorting the reference symbols with respect to the information, to produce a linear transformation signal; and transmitting the linear transformation signal to the receiver. The reference symbols are known at the receiver. The receiver removes the reference symbols from the linear transformation signal and decodes the data-bearing pre-coding transform.

Abstract: A radio transmitter adjusts its radio frequency (RF) fingerprint to defeat RF fingerprinting identification without destroying the content of its transmissions. The radio transmitter comprises a frequency-upconverter configured to upconvert a baseband or intermediate-frequency signal to an RF signal, and an amplifier to amplify the RF signal to produce a transmission signal. An RF fingerprint control circuit changes the non-linear behavior of the frequency-upconverter or the amplifier in order to change the RF fingerprint. The transmitter may create RF fingerprint “personalities” to be paired with different radio protocol behaviors and subscriber terminal identification codes (e.g., MAC addresses or SMSIs) for generating different radio identities.

Abstract: In a computer network that has a plurality of nodes, a measure of trustworthiness for a particular node can be updated by other nodes that monitor the particular node's behavior. This includes collecting trustworthiness reports from the other nodes; updating the particular node's trustworthiness level based on the reports; and causing the particular node to route data in the computer network based on its trustworthiness level. The particular node's role in performing at least one of a set of functions is based on a hierarchy of trustworthiness levels, wherein the functions can include monitoring other nodes; sending alerts when anomalous behavior is detected; transmitting a free-antibody software program to a requesting node; updating defensive programs; participating in consensus-based threat analysis with other nodes; identifying threats; tagging suspicious nodes; and performing countermeasures against identified threats.

Abstract: A system detects and identifies unmanned vehicles (UVs) from radio communications between UVs and their controllers. One or more radio frequency (RF) signal detectors can detect RF signals, including downlink signals transmitted by a UV or uplink signals transmitted by a UV controller. A feature extractor can extract signal features from detected RF signals, and a classifier performs machine learning to identify at least one of the UV and the UV controller based on the signal features. Machine learning can employ an artificial neural network, which may perform deep learning.

Abstract: In a wireless communication system, a secure communication link is provided by producing a set of reference symbol values selected from a modulation symbol constellation; generating a linear transformation operator from information to be transmitted to a receiver; applying the linear transformation operator to the set of reference symbol values, thereby distorting the reference symbol values with respect to the information, to produce a linear transformation signal; and transmitting the linear transformation signal to the receiver. The receiver decodes the linear transformation signal to receive the information.

Abstract: A radio transmitter adjusts the spectral, amplitude, and/or phase characteristics of its transmissions to defeat radio fingerprinting identification without destroying the content of the transmissions. The transmitter determines a threshold of signal distortion that impedes RF fingerprint identification while providing an acceptable amount of degradation to data transmissions and then synthesizes a distortion of its unique RF fingerprint to impede identification. The transmitter may create RF fingerprint “personalities” to be paired with different radio protocol behaviors and subscriber terminal identification codes (e.g., MAC addresses or SMSIs) for generating different radio identities.

Abstract: In a computer network that has a plurality of nodes, a measure of trustworthiness for a particular node can be updated by other nodes that monitor the particular node's behavior. This includes collecting trustworthiness reports from the other nodes; updating the particular node's trustworthiness level based on the reports; and causing the particular node to route data in the computer network based on its trustworthiness level. The particular node's role in performing at least one of a set of functions is based on a hierarchy of trustworthiness levels, wherein the functions can include monitoring other nodes; sending alerts when anomalous behavior is detected; transmitting a free-antibody software program to a requesting node; updating defensive programs; participating in consensus-based threat analysis with other nodes; identifying threats; tagging suspicious nodes; and performing countermeasures against identified threats.

Abstract: A system deploys electronic countermeasures against unmanned aerial vehicles (UAVs) that are determined to be a threat. A radio transceiver coupled to at least one antenna detects if radio signals are being communicated between a remote control unit and a UAV. A mitigation engine communicatively coupled to the radio transceiver synthesizes an exploit signal to be transmitted to the UAV, wherein the exploit signal is configured to inject commands into the UAV's navigation system. If the system determines that the UAV is operating in an autopilot mode, it transmits an induce-mode signal that causes the UAVs to switch to a communication mode so the UAV is responsive to the exploit signal.

Abstract: A system deploys electronic countermeasures against unmanned aerial vehicles (UAVs) that are determined to be a threat. A radio transceiver coupled to at least one antenna detects if radio signals are being communicated between a remote control unit and a UAV. A mitigation engine communicatively coupled to the radio transceiver synthesizes an exploit signal to be transmitted to the UAV, wherein the exploit signal is configured to inject commands into the UAV's navigation system. If the system determines that the UAV is operating in an autopilot mode, it transmits an induce-mode signal that causes the UAVs to switch to a communication mode so the UAV is responsive to the exploit signal.

Abstract: A network node in a distributed network employs a surface immunoglobulin program to monitor other nodes in the distributed network and generate an alert upon detecting a suspicious activity; and pushes a free-antibody program to a requesting node petitioning to access the distributed network. The free-antibody program can include a software agent that monitors the requesting node. The free-antibody program reports detected malware and/or suspicious activity to the surface immunoglobulin program, which can enact countermeasures against the requesting node. The network node's role is based on a hierarchy of trustworthiness levels, wherein it performs at least one of monitoring other nodes, sending alerts when anomalous behavior is detected, transmitting the free-antibody software program to the requesting node, updating defensive programs, participating in consensus-based threat analysis with other nodes, identifying threats, tagging suspicious nodes, and performing countermeasures against identified threats.

Abstract: A network node in a distributed network employs a surface immunoglobulin program to monitor other nodes in the distributed network and generate an alert upon detecting a suspicious activity; and pushes a free-antibody program to a requesting node petitioning to access the distributed network. The free-antibody program can comprise a software agent that monitors the requesting node. The free-antibody program reports detected malware and/or suspicious activity to the surface immunoglobulin program, which can enact countermeasures against the requesting node. The network node's role is based on a hierarchy of trustworthiness levels, its role comprising at least one of monitoring other nodes, sending alerts when anomalous behavior is detected, transmitting the free-antibody software program to the requesting node, updating defensive programs, participating in consensus-based threat analysis with other nodes, identifying threats, tagging suspicious nodes, and performing countermeasures against identified threats.

Abstract: A system deploys electronic countermeasures against unmanned aerial vehicles (UAVs) that are determined to be a threat. A radio transceiver coupled to at least one antenna detects if radio signals are being communicated between a remote control unit and a UAV. A mitigation engine communicatively coupled to the radio transceiver synthesizes an exploit signal to be transmitted to the UAV, wherein the exploit signal is configured to inject commands into the UAV's navigation system. If the system determines that the UAV is operating in an autopilot mode, it transmits an induce-mode signal that causes the UAVs to switch to a communication mode so the UAV is responsive to the exploit signal.