System and method for detecting and mitigating position signal spoofing

Abstract

Systems and methods for detecting and mitigating location signal spoofing. A system (100) for detecting and mitigating location signal spoofing is disclosed: the system (100) includes a plurality of sensors (110) configured to receive location signal data from a plurality of sources and network-based location verification. The system (100) includes a processing unit (120) comprising: a threat profiling engine (130) configured to apply a multi-frequency and sensor fusion method to verify the integrity of the received location signal data. The threat profiling engine (130) uses anomaly detection algorithms to detect spoofing and jamming activities based on the verified location signal data. The processing unit (120) includes a mitigation engine (140) configured to provide mitigation actions including issuing alerts to operators, recommending adjusted positioning, and logging data for forensic analysis.

Description

Technical Field

[0001] Embodiments of this disclosure relate to spoofing attack prevention, and more specifically to systems and methods for detecting and mitigating location signal spoofing. Background Technology

[0002] Location-centric threats such as spoofing, interference, and privacy breaches pose significant challenges to the accuracy, reliability, and security of the Global Positioning System (GPS) and telematics-based navigation systems. These threats are particularly serious in fields that rely on precise location tracking, such as autonomous vehicles, fleet management, aviation, and military operations.

[0003] Current GPS security solutions suffer from several limitations that render them inadequate against modern spoofing and jamming attacks. Existing systems often fail to verify the legitimacy of received GPS signals, making them highly vulnerable to spoofing and signal injection attacks. Most GPS receivers rely on single-frequency signals and lack multi-sensor fusion capabilities, making them unable to distinguish genuine signals from interference. Furthermore, traditional GPS systems use rule-based or threshold-based detection, which is ineffective against sophisticated AI-generated spoofing attacks. Many location-based services fail to incorporate granular privacy controls, exposing users to unauthorized tracking and potential data misuse. Moreover, current solutions are not designed to counteract AI-driven adversarial attacks, deepfake GPS signals, and evolving cyber threats targeting navigation systems.

[0004] Therefore, an improved system and method for detecting and mitigating location signal spoofing is needed to address the aforementioned problems. Summary of the Invention

[0005] According to embodiments of this disclosure, a system for detecting and mitigating location signal spoofing is provided. This disclosure is a robust location security system that employs an enhanced authentication protocol to verify GPS signals in real time, effectively detecting spoofing and interference attempts. By utilizing a hybrid location tracking approach that integrates multi-frequency GPS and sensor fusion with tamper-proof logs, the system ensures continuous and reliable tracking even in challenging environments with signal interference. The system's AI-driven threat detection algorithm proactively analyzes anomalies in location data, distinguishing between normal changes and potential attacks. Furthermore, privacy-enhancing technologies protect user location data from unauthorized access, ensuring compliance with stringent data protection regulations. To address future threats, the system leverages AI-enabled detection mechanisms to counter adversarial GPS spoofing and deepfake-based location manipulation, providing forward-looking defense against evolving cyber threats to navigation systems. Attached Figure Description

[0006] Embodiments of the present invention are described with reference to the following figures: Figure 1 The illustration shows a block diagram representation of a system for detecting and mitigating location signal spoofing according to an embodiment of the present disclosure; Figure 2 The illustration shows a block diagram representation of a privacy-adaptive location data processing system according to an embodiment of the present disclosure; Figure 3 The illustrations depict embodiments according to the present disclosure. Figure 1 and Figure 2 Exemplary embodiments of the system; and Figure 4 This is a flowchart illustrating the steps of a method for detecting and mitigating location data spoofing according to embodiments of the present disclosure. Detailed Implementation

[0007] The invention will now be described by way of example with reference to the accompanying drawings. In all the drawings, the same or corresponding elements are generally indicated by the same reference numerals. These depicted embodiments are to be understood as illustrative of the invention and not as limiting in any way. It should also be understood that the drawings are not necessarily drawn to scale, and embodiments are sometimes illustrated by graphic symbols, dashed lines, schematic representations, and partial views. In predetermined examples, details that are unnecessary for understanding the invention or that make other details difficult to understand may have been omitted.

[0008] The various embodiments described in this document can be implemented by software (e.g., a program or application) that includes one or more machine-readable instructions stored in a storage medium (e.g., memory). For example, a machine's processing unit can invoke at least one of the one or more instructions stored in the storage medium and can execute that instruction. This enables the execution of at least one function based on the invoked at least one instruction. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, "non-transitory storage medium" simply means that the storage medium is a tangible device and does not include signals (e.g., electromagnetic waves), and with regard to this term, there is no difference between the case where data is stored semi-permanently in the storage medium and the case where data is temporarily stored in the storage medium.

[0009] Embodiments of this disclosure relate to systems and methods for detecting and mitigating location data spoofing. As used herein, GPS spoofing involves the transmission of fraudulent satellite signals that trick a GPS receiver into calculating an incorrect location. This can lead to the delivery of incorrect routes in autonomous vehicles, impaired fleet operations, and potential navigation failures. Similarly, interference disrupts the reception of legitimate signals, resulting in navigation errors, loss of positioning accuracy, and potential safety risks such as vehicle collisions. Privacy intrusions occur when unauthorized entities access or manipulate location data, leading to data corruption, user tracking, and violations of privacy regulations such as GDPR and CCPA. Embodiments of the system are described in more detail below with reference to the accompanying drawings.

[0010] Figure 1 This is a block diagram representation of a system (100) for detecting and mitigating location signal spoofing according to embodiments of the present disclosure. The system (100) includes multiple sensors (110) configured to receive location signal data from multiple sources and network-based location verification. This feature ensures that the authenticity of location signals received from multiple sources is verified in real time. This feature combines data from GPS, accelerometers, gyroscopes, compasses, and network-based location verification (e.g., Wi-Fi, LTE). This reduces the chance of location spoofing by cross-validating signals from different sources and analyzing any discrepancies. GPS signals and location data are verified against multiple sources before being accepted as legitimate. This prevents GPS spoofing attacks where erroneous signals mislead the system into calculating incorrect locations. In one embodiment, multi-source verification includes cross-validating GPS signals with sensor fusion data (accelerometer, gyroscope, network signals, etc.) and employing digital signatures or cryptographic keys to verify the authenticity of the GPS signals.

[0011] Furthermore, the system (100) includes a processing unit (120) coupled to multiple sensors (110). The processing unit (120) is configured to operate in hybrid-mode location tracking, ensuring continuous and seamless GPS functionality even in blind spots or environments with signal interference. This is achieved through the integration of multi-frequency GPS tracking, sensor fusion, and tamper-proof location logging. As used herein, blind spots are areas with weak or absent GPS signals, such as tunnels, urban canyons, and underground parking lots; signal interference is interruption caused by environmental factors or malicious interference attacks; and tamper-proof logging includes secure records of location data that cannot be altered without detection.

[0012] The hybrid mode includes offline and online modes. In offline mode, the system logs data locally in a tamper-proof manner, allowing for post-event verification and forensic analysis. In online mode, real-time spoofing detection and intrusion alerts are generated. A key advantage is that if the device loses connection, it continues to track location changes and verifies these changes once the connection is restored. In practice, offline mode can be used in military applications, remote operation, or high-security areas where real-time communication is unavailable or restricted.

[0013] The processing unit (120) includes a threat profiling engine (130) configured to apply multi-frequency and sensor fusion methods to verify the integrity of received location signal data. This threat profiling engine is also configured to detect spoofing and jamming activities based on the verified location signal data using anomaly detection algorithms. Specifically, an AI-based detection algorithm is employed to analyze incoming location data and detect potential threats. The system uses machine learning techniques, including supervised and unsupervised learning models, to distinguish between normal changes and malicious activity. The anomaly detection process involves examining GPS metadata, signal strength, Doppler shift, and historical movement trends. An exemplary embodiment involves using ensemble models, such as random forests and neural networks, trained on real-world and synthetic spoofing scenarios to improve the ability of these ensemble models to detect sophisticated attacks, including adversarial GPS tampering. The training dataset comprises a carefully curated mix of real-world GPS anomalies and synthetically generated spoofing scenarios, covering partial spoofing, meaconing, deepfake signals, and jamming attempts, enabling the AI ​​module to generalize across various adversarial styles. Such algorithms dynamically adapt to identify anomalous styles that may indicate an attack. Pattern recognition technology includes identifying recurring attack patterns, such as forwarding spoofing (delayed GPS signals), and real-time threat analysis includes continuous monitoring of Doppler shift changes, signal consistency, and mobility feasibility.

[0014] The system (100) ensures reliability by cross-referencing multiple sensors, such as GPS, accelerometers, and odometers, to confirm location validity. For example, if GPS signals indicate a sudden change in location that contradicts inertial sensor data, the system flags it as a potential spoofing attempt. This feature is crucial for autonomous vehicles, aviation, and military applications where accurate positioning is essential for safe operation. A confidence score is assigned to each location data point based on its correlation with sensor inputs, historical trends, and environmental factors. If the score falls below a threshold, it triggers an alarm or switches to an alternative positioning method. Additionally, the confidence score affects the severity level of the generated alarms and is securely logged to support continuous learning and forensic auditing. This approach helps minimize false alarms and ensures that action is taken only against significant threats. In one embodiment, a fleet management system can use this feature to prioritize real-time tracking accuracy in high-risk areas. If multiple spoofing attempts occur in a particular area, the system generates a geographic threat map. This enables proactive security measures by flagging high-risk areas for location manipulation attacks. Law enforcement and cybersecurity agencies can use this feature to track emerging threats and enhance situational awareness in vulnerable areas.

[0015] In one embodiment, the AI ​​module uses continuous learning techniques to dynamically adapt to emerging GPS spoofing threats. The module categorizes spoofing types, such as partial spoofing, complete spoofing, and forwarding spoofing (signal interception and retransmission). The system then applies mitigation strategies, such as switching to alternative sources like Wi-Fi or inertial sensors, to maintain accurate tracking. One embodiment relates to an autonomous drone navigation system that detects GPS anomalies and automatically switches to visual-inertial ranging to prevent mission interruption. In some embodiments, a threat profiling engine (130) assigns probability scores to detected anomalies, allowing operators to respond proportionally. For example, a small deviation in GPS data might guarantee monitoring, while a confirmed spoofing attack would trigger immediate security countermeasures. This tiered response approach minimizes unnecessary alerts while ensuring timely intervention in critical situations.

[0016] Additionally, the processing unit (120) includes a mitigation engine (140) configured to provide mitigation actions, including alerting the operator, recommending adjusted positioning, and logging data used for forensic analysis. In one embodiment, the mitigation engine (140) is configured to perform automatic correction of spoofed location data using historical movement patterns and cross-validation with a network-based positioning system. More specifically, upon detecting anomalies or malicious activity in the location signal (e.g., spoofed GPS input), the mitigation engine (140) generates a real-time alert to notify relevant stakeholders. In one embodiment, the alert may be sent via a visual dashboard, SMS / email notification, or in-system warning. The severity of the alert is categorized based on a predefined threat level (e.g., low, medium, high). In such embodiments, the alert includes contextual information such as the type of anomaly detected, current location, signal source ID, and suggested response.

[0017] The mitigation engine (140) provides recommendations for alternative location sources or corrections when location reliability is compromised. The mitigation engine provides guidance on switching to or integrating alternative sources, such as cellular triangulation (3G, 4G, 5G), Wi-Fi access point location data, inertial navigation systems (INS), and pre-mapped geographic data. As used herein, INS is a navigation aid that uses motion sensors (accelerometers and gyroscopes) to calculate positioning, orientation, and velocity. The engine (140) uses multi-source data verification to assess signal credibility. If spoofed data is identified, the engine isolates the compromised input. The engine recommends recalculated positioning based on the verified sources. All detected threats and corresponding system responses are securely logged to create forensic evidence. This capability supports ongoing improvement of investigations into coordinated attacks, regulatory reporting, and threat detection algorithms. In one embodiment, the mitigation engine (140) is enhanced with an automatic correction capability, allowing it to autonomously correct spoofed or unreliable GPS data in real time.

[0018] Finally, signal strength, Doppler shift, and multipath effects are extracted from the GPS and sensor fusion data. The system (100) analyzes real-time patterns and compares them with historical behavior. The system uses classification models (e.g., random forest, SVM, neural network) to determine whether a signal is likely to be spoofed. Furthermore, when the confidence score indicates a high probability of spoofing, the mitigation engine (140) triggers an alarm and continuously retrains the AI ​​model based on new spoofing attempts detected in the system.

[0019] Furthermore, the system (100) is designed to combat AI-enabled location threats, including adversarial GPS attacks and deepfake-based location manipulation. By leveraging AI detection techniques, the system can distinguish between real and fake GPS data. In one embodiment, adversarial GPS spoofing detection includes an AI model trained on synthetic spoofing signals to identify deepfake manipulation. The system (100) compares received GPS coordinates with known topographic maps to detect inconsistencies and continuously learns to improve detection of emerging threats. As used herein, deepfake location manipulation includes synthetic GPS data designed to mimic real movement patterns, and adversarial GPS attacks are AI-generated fake signals designed to bypass conventional spoofing detection methods.

[0020] Figure 2 A block diagram representation of a privacy-adaptive location data processing system (200) according to an embodiment of the present disclosure is illustrated. The system (200) is designed to protect sensitive location data from unauthorized access or misuse, particularly in the context of privacy regulations (such as GDPR, CCPA, etc.), evolving cybersecurity threats, and increasing user awareness of digital privacy. The system combines privacy assessment, protection, controlled sharing, and intelligent surveillance to adaptively manage how the system (100) processes and accesses location data. As used herein, Intelligent Privacy Adaptive Routing (IPAR) is a mechanism that dynamically adjusts the precision of location data based on context and privacy requirements. This adaptive behavior is governed by an Intelligent Privacy Adaptive Routing (IPAR) submodule that dynamically determines the precision and frequency of location data sharing based on geographic sensitivity, operational relevance, user roles, and applicable privacy regulations. The processing system (200) includes a privacy assessment module (210) configured to determine the need for location data sharing based on user preferences and regulatory constraints. This module (210) acts as the decision-making brain of the system, assessing whether and to what extent location data should be shared by considering factors such as whether the user consents to location sharing, the frequency, and the level of precision, while complying with region-specific privacy laws (e.g., GDPR requires explicit consent and limitations on purpose). Specifically, the module continuously evaluates the context of data sharing (e.g., whether it is used for navigation, fleet monitoring, or emergency services). The module categorizes requests as approved, conditionally approved, or denied based on user settings and applicable privacy rules.

[0021] The processing system (200) includes a privacy protection module (220) configured to apply granular location masking based on privacy settings. The privacy protection module (220) is also configured to introduce differential privacy noise to prevent unauthorized tracking. The privacy protection module (220) is further configured to process location data using secure multi-party computation (MPC) without exposing the raw coordinates. As used herein, MPC is a cryptographic technique that allows multiple parties to compute a function on their inputs without revealing the inputs. The module (220) is responsible for modifying and protecting the location data using various advanced techniques before sharing it. The module (220) modifies the accuracy of the location data based on context. The system can provide neighborhood-level accuracy, city-level profiles, and approximate time-of-arrival estimates instead of revealing exact GPS coordinates.

[0022] Furthermore, module (220) applies controlled statistical noise to the location data before sharing to prevent style-based re-identification and ensure that no single data point can be reliably associated with an individual. Module (220) also supports joint data analysis across multiple parties (e.g., logistics providers, city transportation departments) without sharing the original location coordinates. Specifically, the location data is encrypted and divided into multiple parts. These parts are processed across separate computing nodes. The final result is obtained without exposing the original data to any single party.

[0023] The processing system (200) also includes a data sharing module (230) configured to manage location data accessibility using role-based access control (RBAC). As used herein, RBAC is a method of managing access to data or a system based on the roles of individual users. In one embodiment, fine-grained access policies (e.g., "only administrators can view live locations") can be configured, and access logs can be maintained for each request.

[0024] Furthermore, the processing system (200) includes an unauthorized access detection module (240) configured to flag unusual requests for location data and trigger compliance alerts. In one embodiment, the alert and compliance logging mechanism includes generating audit trails about who, when, and how accessed the location data, as well as storing forensic records that support internal audits or regulatory inspections. In some embodiments, the module (240) further enhances privacy by dynamically adapting data precision, such as: In high-security areas, precise coordinates may be completely obscured. For the operating area (such as the delivery area) -- medium precision is permissible. In a user-defined security zone, data can be shared more freely.

[0025] Figure 3 According to embodiments of this disclosure Figure 1 A schematic diagram of an exemplary embodiment (300) of the system (100). Considering a non-limiting scenario, in a busy metropolis, a leading e-commerce company deploys a fleet of autonomous delivery vehicles to ensure timely package delivery. These vehicles rely heavily on GPS-based navigation systems for accurate route planning, obstacle avoidance, and real-time traffic adjustments. However, the security and reliability of navigation systems are at risk due to the increasing prevalence of cyber threats, GPS spoofing, jamming attacks, and privacy breaches. The system (100) provides a robust solution to counteract these threats, thereby ensuring the safe and uninterrupted operation of autonomous vehicles. One of the delivery vehicles (310) is en route to deliver a high-value package when a cybercriminal launches a GPS spoofing attack. The attacker sends false GPS signals that attempt to redirect the vehicle (310) to a remote location for package theft. An authentication protocol immediately identifies anomalies by cross-referencing GPS data with multiple sensors (110) from the vehicle's accelerometer, gyroscope, and network signals. The threat profiling engine (130) of the processing unit (120) detects sudden location jumps that are inconsistent with the vehicle's recent movement trajectory. AI-powered verification of the signal's origin revealed that it did not match a legitimate satellite source. Cryptographic authentication failed because the spoofed signal lacked a valid digital signature.

[0026] Upon detecting a spoofing attempt, the mitigation engine (140) of the processing unit (120) rejects the false GPS data and switches to an alternative positioning method, such as network-based location tracking and motion estimation derived from motion sensors, allowing the vehicle to continue its journey without interruption.

[0027] In a scenario where a vehicle (310) moves through a tunnel, it temporarily loses direct GPS signal due to obstacles. However, the system (100) ensures seamless navigation continuity by employing a multi-frequency GPS system, sensor fusion, and tamper-proof location log recording. The system (100) uses L5 GPS signals, which penetrate obstacles better than standard L1 signals. Inertial navigation (accelerometer + gyroscope) takes over, estimating the vehicle's real-time position. The system (100) predicts the most probable path based on historical movement patterns and road topology. Once the vehicle leaves the tunnel, GPS re-engages, and the system cross-validates its calculated position with the latest satellite data to ensure accuracy. This hybrid approach prevents misrouting and eliminates errors caused by temporary signal loss.

[0028] In one embodiment, when the delivery vehicle (310) enters a high-risk area vulnerable to cyberattacks, the threat profiling engine (130) proactively monitors GPS signal integrity and compares the received signals with historical patterns. Suddenly, an attacker attempts a low-power jamming attack, trying to overwhelm the vehicle's GPS receiver with interference. The threat profiling engine detects sudden fluctuations in signal strength and an unusual increase in background noise. AI-driven analysis flags the event as a likely jamming attack based on past attack signatures. The mitigation engine (140) activates anti-jamming measures, filtering out interference and switching to alternative positioning sources, such as cellular triangulation. This ensures that the vehicle maintains its course and does not lose navigation capability, even in harsh environments.

[0029] In one scenario, delivery vehicles (310) carry sensitive customer data, including real-time location updates shared with fleet managers and customers. However, hackers attempt to intercept location transmissions to track vehicle routes and predict high-value deliveries. The system (200) encrypts the location data before transmission, using a privacy assessment module (210) to make the location data unreadable to unauthorized entities. Differential privacy techniques introduce subtle variations in the shared location data to prevent precise tracking, while a privacy protection module (220) maintains operational functionality. Role-based access control (RBAC) ensures that only authorized personnel can access the precise delivery location, while customers use a data sharing module (230) to receive approximate delivery time ranges without exposing precise coordinates. This privacy enhancement mechanism uses an unauthorized access detection module (240) to protect the company's logistics operations from corporate espionage and unauthorized tracking. As used herein, privacy enhancement mechanisms incorporate technologies designed to protect personal data and ensure compliance with privacy regulations such as GDPR and CCPA.

[0030] Figure 4 This is a flowchart illustrating the steps involved in a method (400) for detecting and mitigating location data spoofing according to an embodiment of the present invention. The method includes receiving location signal data from multiple sources and network-based location verification in step 410. In one embodiment, receiving location signal data from multiple sources includes satellite-based systems (such as GPS, Galileo, or GLONASS) and network-based location services such as Wi-Fi positioning, LTE triangulation, or Bluetooth beacons. This multi-source architecture ensures that the system does not rely on a single potentially compromised input and lays the foundation for resilient location verification.

[0031] The method (400) also includes collecting sensor fusion data from accelerometers, gyroscopes, odometers, compasses, and network signals in step 420. This data provides rich contextual information about the physical behavior of the object, including direction of movement, orientation, velocity, and inertial changes, enabling the system to construct a cross-validation profile of actual location activity versus reported location activity.

[0032] Furthermore, the method (400) includes extracting key location and movement features in step 430, including speed, direction, time deviation, and multipath effects. In such embodiments, extraction may include the system extracting key location and movement features, including but not limited to speed, direction of movement, time synchronization or deviation from expected timestamps, and multipath signal characteristics that can reveal whether a signal is bouncing off a surface or being manipulated by a malicious entity. Anomalies in these features are then analyzed, with particular attention to signal inconsistencies, suspicious changes, or delays that deviate from known patterns of legitimate navigation behavior. For example, sudden and unexplained jumps in location data, unexpected reversals in direction, or signal path planning incompatible with known geography may indicate a spoofing attempt.

[0033] The method (400) further includes analyzing anomalies in the characteristics of the GPS signal in step 440 to detect spoofing attempts. In one embodiment, analyzing these anomalies may include: analyzing patterns in the location data to distinguish between normal changes and potential threats, cross-referencing the location signal with sensor data to detect inconsistencies, applying deviation thresholding to identify suspicious location jumps, and leveraging AI-based pattern recognition to detect adversarial location data and deepfake attacks. In such an embodiment, if the GPS signal indicates rapid movement while the odometer reports traveling a negligible distance, a spoofing attempt may be suspected. The method also employs threshold deviation detection, where any location offset exceeding an expected tolerance level triggers flags and AI-based pattern identification to specifically detect signal synthesis in adversarial location data attacks or deepfake schemes, where attackers use generative models to simulate real but false locations.

[0034] Furthermore, the method (400) includes applying a classification model in step 450 to determine the likelihood of a spoofing attack. The method (400) also includes generating an anomaly probability score in step 460 to assess the severity of the attack. In one embodiment, a trained AI-based classification model is applied, which evaluates collected features and determines the likelihood of a spoofing attack. This model may be based on supervised learning techniques, such as random forests, SVMs, or neural networks trained on historical spoofing and non-spoofing data. The model generates an anomaly probability score that reflects the severity or confidence level of the detected threat. A high score indicates a strong indication of spoofing, prompting the system to initiate mitigation.

[0035] Subsequently, the method (400) includes providing mitigation actions in step 470, including alerting operators, recommending adjusted locations, and logging data for forensic analysis. These mitigation actions encompass a range of defensive and corrective measures, such as alerting operators (e.g., notifying fleet managers or vehicle systems), recommending adjusted or alternative locations (derived from trusted sensor and network sources), and logging all events for forensic analysis and compliance review. This not only helps in responding to immediate threats but also helps prepare for regulatory audits and improve future models.

[0036] In some embodiments, the method (400) includes updating an adaptive model to retrain the classification model when a new spoofing pattern is detected. When a labeled anomaly is verified as an attack, these data points are fed back into the training pipeline, allowing the adaptive model to be retrained and its detection accuracy improved. This adaptive modeling ensures that the system evolves with the adversary, thereby maintaining its effectiveness against emerging threats such as AI-enabled spoofing, RF manipulation, and synthetic location injection.

[0037] Various embodiments of systems for detecting and mitigating location data spoofing implement a multi-layered security framework that enhances the reliability, accuracy, and resilience of location-based systems. This framework addresses the limitations of existing GPS and telematics solutions by integrating advanced authentication mechanisms, hybrid tracking methods, AI-enabled anomaly detection, privacy enhancement technologies, and timeless countermeasures against evolving threats. These technological advancements significantly improve the security of autonomous driving navigation, fleet management, and other location-related applications.

[0038] This system offers an enhanced authentication protocol that prevents GPS spoofing and jamming attacks. Traditional GPS receivers lack the ability to authenticate signals, making them vulnerable to malicious broadcasts of false locations. This system addresses this issue by implementing multi-source authentication, cryptographic authentication of GPS signals, and an AI-based spoofing detection algorithm. This advancement enhances the accuracy and security of positioning systems, making them resistant to cyberattacks. Furthermore, the system eliminates reliance on a single tracking technology, allowing autonomous vehicles, drones, and navigation systems to operate reliably even without GPS.

[0039] Furthermore, the system offers an AI-powered threat detection system that dynamically analyzes GPS signals to detect and mitigate real-time attacks. This advancement significantly reduces false positives, shortens response times, and enhances the cybersecurity of location-based applications. The system introduces advanced privacy-preserving technologies to ensure the security and controlled access to sensitive location data. This progress supports privacy-compliant location tracking while maintaining high security for commercial and government applications.

[0040] Furthermore, the system is designed to combat AI-enabled threats through deepfake location detection, where AI models analyze signal consistency, mobility feasibility, and terrain matching discrepancies to reject manipulated GPS data. AI-enabled cross-validation with a geospatial database ensures that reported locations match real-world geographical conditions. Adaptive AI learning continuously updates the detection model based on emerging GPS spoofing techniques. This timeless security architecture ensures that autonomous vehicles, smart city infrastructure, and defense systems can withstand next-generation cyber threats.

[0041] It should be understood that the embodiments described above are merely illustrative and do not limit the scope of protection of the invention. Many such embodiments and other modifications and variations of the embodiments explained in the specification are contemplated. The scope of protection of the invention is defined only by the scope of the claims.

Claims

1. A system (100) for detecting and mitigating location signal spoofing, comprising: Multiple sensors (110) are configured to receive location signal data from multiple sources and network-based location verification; Its features are, A processing unit (120) coupled to the plurality of sensors (110), wherein the processing unit (120) includes: The threat profiling engine (130) is configured as follows: Multi-frequency and sensor fusion methods are applied to verify the integrity of the received location signal data; Anomaly detection algorithms are used to detect spoofing and interference activities based on verified location signal data; and A mitigation engine (140) is configured to provide mitigation actions, including alerting the operator, recommending adjusted positioning, and logging data used for forensic analysis.

2. The system (100) of claim 1, wherein the processing unit (120) is configured to operate in at least two modes, including: Offline mode is used to record and verify location changes through tamper-proof logging and post-event AI-driven analysis; as well as Online mode is used to provide real-time spoofing detection and intrusion alerts by continuously analyzing geolocation anomalies.

3. The system (100) of claim 1, wherein the threat profiling engine (130) is configured to assign confidence scores to potential GPS spoofing attempts based on anomalies in signal strength, Doppler variations, and inconsistencies in movement.

4. The system (100) of claim 1, wherein the mitigation engine (140) is configured to perform automatic correction of spoofed location data using cross-validation of historical movement patterns and a network-based positioning system.

5. A method (400) for detecting and mitigating location data spoofing, comprising: Receive location signal data from multiple sources and network-based location verification (410); Collect sensor fusion data from accelerometers, gyroscopes, odometers, compasses, and network signals (420). Extract key location and movement features, including velocity, direction, time deviation and multipath effect (430). Analyze anomalies in GPS signal characteristics to detect spoofing attempts (440); A classification model was used to determine the likelihood of a spoofing attack (450). Generate anomaly probability scores to assess the severity of the attack (460); and Provide mitigation measures, including alerting operators, recommending adjusted locations, and logging data used for forensic analysis (470).

6. The method (400) according to claim 5, wherein analyzing the anomaly comprises: Analyze the patterns in the location data to distinguish between normal changes and potential threats; Cross-referencing the position signal with sensor data to detect inconsistencies; Apply deviation threshold processing to identify suspicious location jumps; as well as Utilize AI-based pattern recognition to detect adversarial location data and deepfake attacks.

7. The method (400) of claim 5, further comprising updating the adaptive model to retrain the classification model when a new deception pattern is detected.

8. A privacy-adaptive location data processing system (200), comprising: The privacy assessment module (210) is configured to determine the need for location data sharing based on user preferences and regulatory constraints; The privacy protection module (220) is configured as follows: Apply granular location masking based on privacy settings; Differential privacy noise is introduced to prevent unauthorized tracking; as well as Use secure multi-party computation to process location data without exposing the raw coordinates; The data sharing module (230) is configured to manage the accessibility of the location data using role-based access control; as well as The unauthorized access detection module (240) is configured to flag unusual requests for location data and trigger compliance alerts.

9. The system (200) of claim 8, wherein the location data is shared at different levels of precision based on security zones, operational requirements, and user preferences.

10. The system (200) of claim 8, wherein the unauthorized access detection module is configured to support alarm and compliance logging mechanisms to generate audit trails to track location data access and detect anomalies.

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