Anti-Spoofing Methods for UAV Communications

UAVs equipped with neural networks to resist electronic attacks that try to hijack or interfere with their flight. The UAVs have onboard neural networks that analyze real-time sensor data to detect cyber attacks and respond appropriately. The networks analyze images, GPS, and communications to identify attacks like spoofing, jamming, or disorientation. They can then autonomously mitigate the attacks, like continuing the flight path or seeking better connectivity, without being compromised.

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Detecting and taking countermeasures against unmanned aerial vehicles (UAVs) using AI-assisted object classification and adaptive jamming/spoofing. The system receives signals reflected by objects, conditions them to improve resolution and SNR, then uses AI to classify if the object is a UAV. If so, it takes over control using jamming and spoofing. Jamming disorients the UAV for a time, then spoofing establishes communication to remotely maneuver it. The jamming and spoofing parameters adapt based on range and velocity.

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A UAV with a GPS spoofing detection system that enables autonomous flight control by monitoring GPS signal integrity. The system employs a 3-axis magnetometer to measure aircraft orientation and compare it with GPS heading data. When the two measurements do not match, the system detects potential GPS spoofing and triggers corrective actions, such as returning to a safe location. This integrated GPS and magnetometer system provides a comprehensive solution to GPS spoofing protection in UAVs.

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A system for detecting GNSS signal spoofing using static IoT devices. The system comprises a network of static IoT devices that continuously monitor GNSS signals and compare short-term and long-term averages of GNSS fixes. When a device detects a discrepancy between the two averages, it sends a warning message to a server, which analyzes the data to determine if a spoofing condition is present. If confirmed, the server sends a spoofing alert message to affected user equipment, providing location information of the spoofer and a spoofed zone.

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A system and method for detecting spoofing or jamming of GNSS signals using time-differenced carrier phase (TDCP) computed GNSS acceleration compared with acceleration estimates derived from inertial navigation systems (INS). The system computes GNSS-derived acceleration estimates and INS-derived acceleration estimates at multiple time epochs, and generates a time-varying threshold signal based on user-specified probability of false alarm or probability of missed detection. A spoofing or jamming alert is generated when the magnitude of the difference signal between the GNSS-derived acceleration estimates and INS-derived acceleration estimates exceeds the threshold signal.

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A method and system for detecting and mitigating GNSS spoofing threats in aircraft navigation systems. The system monitors satellite signals and compares predicted orbital information with actual values to detect anomalies indicative of spoofing. Spoofing alerts are generated and displayed to pilots, enabling corrective action to prevent navigation system failures and ensure safe flight operations.

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A GNSS receiver with anti-spoofing capability that uses a beamformer to generate multiple downconverted signals, a processor to identify authentic signals, and GNSS signal replicators to generate replicas of authentic signals based on timing settings used to track them. The processor determines which signals are authentic by analyzing the signal strength variation when the beam pattern is dithered.

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A system and method for detecting and alerting aircraft to potential GNSS spoofing threats in a protected airspace surrounding an airport. The system comprises multiple receiver stations with GNSS receivers and antennas at surveyed locations, which continuously monitor GNSS signals and compare their determined positions to their true locations. If a receiver station detects a nonzero probability of spoofing, it reports to a master control station, which transmits regular updates to aircraft within range, enabling them to adjust their protection levels accordingly. The system can also detect spoofing sources and provide directional information to aircraft.

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Reducing GNSS signal processing power to prevent positioning errors caused by satellite spoofing. The technique operates the receiver in reduced operational mode when spoofing is detected, specifically disabling critical processing functions like data demodulation, time setting, and error recovery. This enables the receiver to operate in a reduced state with respect to the GNSS bands most likely to be spoofed, thereby mitigating the adverse effects of spoofing on positioning and timing applications.

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A GNSS receiver that detects and mitigates spoofing attacks using a controlled reception pattern antenna (CRPA) with digital beamforming. The CRPA generates multiple survey beams that simultaneously steer a null towards the spoofing source and a beam towards the true GNSS signals. The receiver determines the presence of a spoofer by analyzing the carrier-to-noise ratio (C/No) signatures of multiple GNSS signals, and uses the CRPA's beamforming capabilities to isolate and reject the spoofing signals.

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A GNSS receiver that detects and mitigates spoofing attacks using digital beamforming and null steering. The receiver generates multiple antenna patterns to survey the environment and identifies spoofing signals by detecting signal power anomalies. It then steers a null to cancel out the spoofing signals, preventing them from affecting the receiver's position and velocity estimates. The receiver also determines its attitude by associating signal strength patterns with the locations of GNSS satellites.

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System and method for detecting GNSS spoofing using carrier-to-noise ratio (C/No) monitoring. The system calculates C/No comparison values based on received GNSS signal measurements and compares them against previous values to detect anomalies indicative of spoofing. The system also monitors for sudden C/No increases, common C/No decreases across multiple satellites, and expected C/No values based on satellite geometry to identify spoofing attempts.

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Method for identifying true and falsified GNSS signals that permits continued navigation based upon the true signals. The method detects a plurality of GNSS carrier signals, temporally tracks characteristics of the plurality of GNSS carrier signals for dither, compares the dither data, identifies at least one falsified GNSS carrier signal based upon a degree of correlation of the dither data, and determines navigation information based on the plurality of GNSS carrier signals not identified as falsified GNSS carrier signals.

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Tamper-resistant datalink communications system for drones that detects and prevents unauthorized interference and hacking through a dual-transmitter, dual-receiver architecture with frequency diversity and signal duplication. The system converts and duplicates control signals transmitted from a ground controller to a drone, with verification signals sent back to the ground station to confirm successful receipt. This architecture provides enhanced security and reliability for drone operations.

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Detecting spoofing attacks on GPS receivers through a novel triangle-based signal processing approach. The method employs a sparse optimization algorithm to identify the unique components of a received signal's correlator output, distinguishing between genuine GPS signals and spoofing attacks. The approach leverages pre-computed discrete-time waveform functions stored in a memory device, which are used to model the signal's correlator output. By analyzing the sparse vector of these pre-computed functions, the algorithm can selectively identify the spoofing signal's components, enabling accurate detection of spoofing attacks.

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A system for detecting GNSS signal spoofing using static IoT devices. The system comprises a network of static IoT devices that continuously monitor GNSS signals and compare short-term and long-term averages of GNSS fixes. When a device detects a discrepancy between the two averages, it sends a warning message to a server, which analyzes the data to determine if a spoofing condition is present. If confirmed, the server sends a spoofing alert message to affected user equipment, providing location information of the spoofer and a spoofed zone.

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Method for detecting spoofing of satellite signals in aircraft navigation systems, comprising receiving an apparent satellite signal, determining characteristic signatures such as power level and secondary characteristics, comparing these signatures to current transmission data values, and indicating spoofing when the difference exceeds a predetermined tolerance.

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A decentralized trust assessment system (DTAS) for aircraft avionics that combines a trust assessment module with a neural network to verify the integrity of input and output data streams, detect anomalies, and prevent spoofing attacks. The system integrates a trust module's specific programming with the neural network's pattern recognition capabilities to provide comprehensive data validation and ensure reliable aircraft operation.

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A system and method for detecting and mitigating spoofed satellite navigation signals. The system includes a GNSS receiver, a memory, and one or more processors that determine a first location associated with a location determined based on GNSS signals prior to the likely spoofing of the at least one GNSS signal, and determine a second location based on one or more locations determined after determining the spoofing has ceased. The method includes determining a first location associated with a location determined based on GNSS signals prior to the likely spoofing of the at least one GNSS signal, determining a second location based on one or more locations determined after determining the spoofing has ceased, and determining a spoofing region based on the first and second locations.

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A method for mitigating GNSS signal spoofing in a receiver, comprising operating the receiver in a reduced operational state when spoofing is detected, wherein the reduced operational state includes disabling data demodulation and decoding, disabling time setting, disabling acquisition of unknown satellites, disabling satellite differencing, disabling error recovery, reducing non-coherent integration time, and duty cycling the power for receiver blocks associated with the spoofed band.

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