Drone Anti Spoofing Solutions

Detecting GPS spoofing attacks on drones using a fog computing network. The method involves sending drone signals to a fog node for analysis. The fog node uses machine learning models to detect slow shifting patterns indicative of spoofing attacks. This allows fog nodes to detect spoofing attacks on drones in a decentralized network.

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Intercepting and controlling drones without damaging them by redirecting them to land using spoofing signals. The method involves detecting unauthorized drones near restricted areas, deploying police drones to intercept them, and transmitting signals to program new flight paths to land. The redirecting signals include spoofing GPS signals to simulate satellite constellations and jamming signals to disable the target drone's controls. The police drones position themselves close to the targets and then transmit the redirect signals to safely guide the intruders to designated landing areas.

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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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Verifying the position of unmanned aerial vehicles (UAVs) to improve security and accuracy in situations where GPS signals are unreliable. The techniques include: comparing reported positions against known signal fingerprints at that location, measuring signals from the UAV and comparing to known fingerprints, requesting the UAV to transmit signals from its location, and detecting GPS spoofing by comparing UAV-reported signals to known fingerprints.

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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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Distributed trust management for mobile computers like drones operating in sensitive environments to prevent attacks like false updates, malicious commands, and joining by imposter drones. The method involves each drone using a state machine replication protocol to locally assess the trustworthiness of other drones. When a drone wants to perform a sensitive operation, it requests approval from other drones. The requester's trust level is determined based on the assessments. The requester's trust level is updated based on the global assessment. This allows distributed trust verification without central coordination.

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A system and method for autonomous cyber attack detection, localization and neutralization in UAVs to protect them from cyber attacks without major redesigns, additional sensors or redundant systems. It uses machine learning to detect attacks by monitoring key sensor signals and looking for abnormal behavior outside normal operating ranges.

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Anti-drone system that enables precise and efficient drone neutralization through GPS spoofing analysis. The system analyzes drone features, such as safety mechanisms and flight algorithms, to determine the most effective hijacking strategy. It then injects a GPS spoofing signal to the target drone, which automatically adjusts its flight path based on the identified strategy. This approach ensures accurate and precise neutralization of drones without disrupting their normal operation, while minimizing collateral damage and maintaining situational awareness.

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Secure command transmission between unmanned aerial vehicles (UAVs) through a novel encryption approach that preserves the unique characteristics of each UAV while maintaining command integrity. The method employs fixed and dynamic feature parameters for encryption, enabling secure command transmission between UAVs without compromising their operational capabilities. Each UAV stores its unique feature parameters in an external device, which establishes wireless communication. The external device encrypts commands using the UAV's fixed parameters, while the UAV verifies the commands through its dynamic parameters. This approach ensures secure command transmission while preventing replay attacks and maintaining accurate execution.

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Secure communication between a remote computing system and an unmanned aerial vehicle (UAV) through a novel cryptographic approach. The system employs a UAV controller with a secure processor and communications module to establish encrypted communication channels between the controller and the flight management system. The controller extracts UAV-specific data from messages, computes a shared secret key, and maintains a database of encrypted keys. When receiving a message, the controller verifies the message's integrity by comparing its encrypted payload with a computed hash. This ensures secure data transmission between the controller and the flight management system, enabling real-time control of the UAV while maintaining confidentiality.

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Aircraft flight control system detects GPS spoofing by monitoring deviation from scheduled flight route. The system continuously measures aircraft position and compares it to planned route, triggering spoofing detection when deviation exceeds a threshold. This enables early identification of GPS signal tampering, allowing prompt corrective action to prevent navigation errors.

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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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Authenticating signals using Doppler nulling to improve resilience against spoofing and Denial of Service attacks compared to conventional methods. The technique involves scanning for signals using Doppler nulling to determine their parameters like time of arrival and frequency. By resolving the temporal spatial characteristics of the transmitter's radiation, it provides spatial awareness of relative speeds and directions. This information can then be used to authenticate received signals by comparing their parameters to the expected ones determined during the scanning. The technique allows low susceptibility to spoofing and Denial of Service attacks since it relies on the specific temporal characteristics of the transmitter's motion.

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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 down converted 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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