AI-Based RF Anomaly Detection for Counter-UAS

Extending the range of detection for detecting and disrupting unauthorized drones using a network of distributed drone disruption units. The method involves monitoring drones and their controllers in multiple areas using local drone disruption units. If a drone is detected in a monitored area, nearby drone disruption units are notified. They then collaboratively disrupt the drone's communication link to force it to land. This leverages local resources to extend the effective range of drone detection and disruption beyond the capabilities of individual systems.

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Modular RF systems for locating, identifying, and tracking objects in 3D space using directional antennas. The systems have compact, movable enclosures with integrated cooling and thermal management. The enclosures house modules like RF, power, and processing. Directional antennas are attached to the exterior. The systems leverage machine learning to identify object types from captured RF signals. Based on the type, they generate customized signals and transmit them from the directional antennas. This allows targeted transmissions vs omnidirectional. Multiple systems can share custom signals. The modular design allows swapping antennas for different applications.

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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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A smart surveillance network for detecting and tracking non-cooperative and cooperative air and watercraft, enabling safe beyond-visual-line-of-sight (BVLOS) operations of unmanned aircraft systems (UAS) and unmanned surface vessels (USV). The system integrates radar, Automatic Dependent Surveillance-Broadcast (ADS-B), Automatic Identification System (AIS), and counter-UAS sensors to provide real-time situational awareness, automatic sensor performance assessment, and risk-based traffic analysis. It enables authorities to certify airspace or waterways for BVLOS operations, monitor compliance with regulations, and detect potential safety threats.

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Passive sensing of unmanned aircrafts (UAs) using forward scatter effect of radio frequency (RF) signals transmitted by satellites and/or aerostats. The system comprises passive RF sensor units that receive and process RF signals to detect and track UAs within a target volume. Multi-sensor analysis units and a central analysis unit process data from multiple sensor units to provide UA situational surveillance, including target detection, tracking, classification, and event prediction.

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A method for detecting micro-drones in urban environments using a combination of active and passive sensors. The method involves recording acoustic signals from the drone's propellers and optical signals from the drone's mechanical vibrations, and then correlating these signals to detect the drone. The active sensors include a millimeter-wave radar operating in the range of resonant absorption of radio waves in the atmosphere, and an active acoustic sensor operating in the range of resonant scattering of sound waves on the drone. The passive sensors include an optical sensor operating in the visible and infrared ranges. The method enables reliable detection of micro-drones in dense urban environments with high levels of interference.

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Detecting flying objects beyond visual line of sight (BVLOS) using computer vision on radar data to enable safe beyond-line-of-sight drone operation. The method involves converting radar buffer images into radial frames, subtracting a background, denoising, tracking objects, and identifying flying objects using CV techniques like contour detection. This allows detecting and avoiding other aircraft in BVLOS airspace by processing radar data like a radar screen would be seen by an operator.

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Unmanned aerial vehicles (UAVs) are becoming more widely used in various industries, raising growing concerns about their safety and reliability. The flight data of UAVs can directly reflect their flight health status; however, the rarity of abnormal flight data and the spatiotemporal characteristics of these data represent a significant challenge for constructing accurate and reliable anomaly detectors. To address this, this study proposes an anomaly detection framework that fully considers the temporal correlations and distribution characteristics of flight data. This framework first combines a one-dimensional convolutional neural network (1DCNN) with an autoencoder (AE) to establish a feature extraction model. This model leverages the feature extraction capabilities of the 1DCNN and the reconstruction capabilities of the AE to thoroughly extract the spatiotemporal features from UAV flight data. Then, to address the challenge of adaptive anomaly detection thresholds, this research proposes a nonlinear model of support vector data description (SVDD) utilizing a 0/1 soft-margin loss,... Read More

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A method and system for identifying aerial vehicles using passive electromagnetic scattering structures attached to rotors. The structures generate unique micro-Doppler signatures during rotation, which are analyzed using machine learning algorithms to identify the vehicle. The system enables reliable identification of small aerial vehicles without active transponders, providing a passive and secure solution for friend-or-foe identification and tracking in shared airspace.

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Integrated system for low-altitude drone response that enables rapid identification and mitigation of unauthorized drones. The system combines radar, camera, and communication capabilities to detect and analyze drone presence, determine threat classification, and initiate response actions. The system integrates with a central server to manage drone traffic, including automatic threat detection, tracking, and response capabilities.

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Advancements in unmanned aircraft systems (UASs) have led to a considerable increase in unlawful operations of these systems over critical infrastructures and public spaces. In this work, a system that is implemented utilizing software-defined radio and signals of opportunity (SOPs) and is based on the fusion of direction of arrival and passive radar sensor measurements is proposed to counter unlawful UAS/drone activities. Specifically, passive radar methodology applied on SOPs (data collection, disturbance cancellation, cross-ambiguity, and constant-false alarm rate detection functions), along with direction of arrival (DOA) measurements obtained via the multiple signal classification algorithm, are combined to detect and track the rogue UAS/target in an area under observation. A prototype implementation of the detection-and-tracking system, with the use of small embedded processing units and the robot operating system is developed and examined in numerous outdoor experiments to thoroughly evaluate its performance.

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Small Uncrewed Aerial Systems (sUAS), commonly referred to as drones, have become ubiquitous in many domains. Examples range from drones taking part in search-and-rescue operations to drones being used for delivering medical supplies or packages. As sUAS commonly exhibit safety-critical behavior, ensuring their safe operation has become a top priority. Thus, continuous and rigorous monitoring of sUAS at runtime is essential. However, sUAS generate vast amounts of data, for example, multi-variate time series which need to be analyzed to detect potential emerging issues. This poses a significant challenge, due to resource constraints imposed on the onboard computation capabilities of sUAS. To alleviate this problem, we introduce ADAM, a novel adaptive monitoring anomaly detection framework for sUAS. ADAM selectively monitors a subset of data streams, which serve as indicators of anomalous behavior. In the event of a raised alert, ADAM adjusts its monitoring strategy, enabling additional detectors and taking further mitigation actions. We evaluated the effectiveness of ADAM through simu... Read More

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Unmanned Aerial Vehicles (UAVs) gather data efficiently for power line inspection. Anomaly detection is essential for power infrastructure dependability and security. It proposes a Cloud-Enabled Isolation Forest (CEIF) method for UAV-based power line inspection. It improves the isolation forest algorithm's efficiency and scalability in cloud computing. It can process huge UAV inspection datasets by dispersing cloud computing. The technique, which effectively isolates anomalies, is applied to the cloud for fast power line inspection and anomaly identification. It describes the CEIF system's cloud service integration and distributed computing algorithm optimization. Real-world UAV-based power line inspection datasets show it can accurately detect abnormalities with low false-positive rates. It is scalable and robust for improving power infrastructure dependability and security. It allows cloud services to deploy real-world settings to implement different inspection scales.

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Unmanned aerial vehicles (UAVs) are predicted to be widely used in both military and civilian sectors in the coming years due to their high mobility, low cost, and enhancement of the line-of-sight (LoS) conditions in non-terrestrial networks.Nevertheless, this raises some security issues if they are manipulated to cause security threats in restricted locations or to breach user privacy.In order to detect malicious UAVs, radio frequency (RF)-based approaches are adopted to detect ambient RF signals, which can be accomplished with inexpensive RF sensors under both LoS and, in particular, non-line-of-sight (NLoS) conditions.In this paper, we propose a passive detection technique based on received signal strength (RSS), and derive analytical expressions on the detection and false alarm probabilities considering realistic airto-ground (A2G) channel conditions.A novel low-complexity suboptimal detector is also proposed and its performance is compared to the optimal detection.Monte Carlo simulations are used to confirm the accuracy of the derived expressions under the aforementioned channel... Read More

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Anomaly detection for flight data is crucial in maintaining the safety and stability of unmanned aerial vehicles (UAVs), making it a topic of significant research and attention. However, existing anomaly detection methods often ignore the random noise of UAV flight data and lack effective parameter selection, resulting in inadequate anomaly detection performance. Furthermore, current methods generally face the problem of insufficient feature extraction capability. In this paper, a spatio-temporal correlation based on one-dimensional convolutional neural network (1D CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM) hybrid neural network with residual filtering (STC-1D CBiAM-RF) data-driven multiple regression framework is proposed for anomaly detection and recovery of UAV flight data. First, a correlation analysis method is used for parameter selection to reduce the dependence on expert knowledge. Second, a multiple regression model fusing attention mechanism is designed. It utilizes 1D CNN-BiLSTM as a feature extractor, guided by the attention mechanis... Read More

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The use of Unmanned Aerial Vehicle (UAV) in various industries is increasing, which places higher requirements on the reliability of UAV. One of the ways to ensure the safety of UAV flights is by detecting anomalies in their flight. However, traditional UAV anomaly detection models have some shortcomings. First, they fail to integrate data from multiple sensors across time and frequency domains, hampering the anomaly detection model's ability to accurately assess the UAV's status. Second, they apply the same prediction error loss to all classes, which result in excessive false positives in some key classes. Finally, most of them used unimodal classification models to process data from multiple heterogeneous sensors, which makes it difficult for the models to extract targeted features. This paper proposes a UAV anomaly detection model based on sensor information fusion and hybrid multimodal neural network (IF-HMNN). Firstly, facilitated by the newly devised Multi-source Heterogeneous UAV Sensor Information Alignment algorithm (MHSIA), IF-HMNN can realize information fusion from multip... Read More

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In recent years, a surging number of unmanned aerial vehicles (UAVs) are pervasively utilized in many areas. However, the increasing number of UAVs may cause privacy and security issues such as voyeurism and espionage. It is critical for individuals or organizations to manage their behaviors and proactively prevent the misbehaved invasion of unauthorized UAVs through effective anomaly detection. The UAV anomaly detection framework needs to cope with complex signals in the noisy-prone environments and to function with very limited labeled samples. This paper proposes <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BisSiam</small> , a novel framework that is capable of identifying UAV presence, types and operation modes. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BisSiam</small> converts UAVs signals to bispectrum as the input and exploits a siamese network based contrastive learning model to learn the vector encoding. A sampling mechanism is proposed for optimizing the sample size involved... Read More

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A cognitive electronic warfare (EW) system that learns to counter unknown radar threats in real-time without relying on pre-existing threat databases. The system uses machine learning to analyze radar waveforms, identify emitter characteristics, and determine threat intent. It then generates and optimizes electronic countermeasures (ECMs) based on observed threat behavior, continuously assessing their effectiveness and adapting the ECM strategy as needed. The system operates in a closed-loop fashion, enabling real-time countermeasures against agile and unknown radar threats.

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A system and method for detecting and locating unauthorized unmanned aerial systems (UAS) in restricted airspace around wildfires. The system, called a Mobile Emergency Perimeter System (MEPS), uses multilateration and mesh networking to quickly locate the source of radio frequency signals controlling the UAS. MEPS can be rapidly deployed in various terrain conditions and extreme environments to establish a mobile perimeter around wildfires, enabling authorities to identify and halt unauthorized UAS operations.

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Unmanned aerial vehicle (UAV) detection, tracking, and management system that utilizes a combination of radar, video, audio, and camera sensors to monitor airspace. The system employs advanced object detection algorithms to identify and track UAVs while maintaining accurate spatial relationships. It also enables real-time monitoring of UAVs through camera-based tracking, with automatic camera reorientation to maintain object center-of-view. The system maintains a comprehensive database of detected UAVs, including historical trajectories, to enhance object recognition and tracking capabilities.

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A method for recovering original radio signals from Doppler-shifted and time-shifted signals received at mobile sensing devices, such as satellites or drones, using machine learning. The method trains a network with labeled time series data representing RF signals and their characteristics, and uses the trained model to classify new signal sequences and recover the original signals. The approach enables computationally efficient transform-stripped signal recovery, emitter identification, and geolocation based on signals received at one or multiple sensing devices.

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System and method for disrupting radar threats that are unidentified, ambiguous, or not effectively countered by known countermeasures. The system employs a cognitive electronic warfare (CEW) system that analyzes the radar waveform, identifies potential countermeasures, and optimizes their parameters through machine intelligence and reinforcement learning. The CEW system can apply multiple countermeasures in combination, varying their parameters to achieve optimal disruption of the radar threat.

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With the rapid development of artificial intelligence, unmanned aerial vehicle (UAV) has been widely applied in multiple fields for their convenience, low cost, and powerful function. Compared with single-UAV data, the UAV fleet data has more rich features, which can provide greater data collection and rich data information support to achieve more accurate anomaly detection. However, the flight paths of different UAVs vary greatly, which brings many challenges to fleet-level data analysis. And it is difficult for UAV fleet data to get exception labels to accurately build an anomaly detection model. Based on the above problems, this paper presents an anomaly detection method based on multivariate state estimation (MSET) and density-based spatial clustering of applications with noise (DBSCAN) optimization (DBSCAN- MSET). Establishing a normal state representation model from historical UAV data can effectively detect whether the following flight is abnormal. The proposed method is validated against the actual UAV data, and the results show the validity of our method.

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A surveillance system for detecting and classifying off-the-shelf drones in a local area using RF signal detection and machine learning. The system analyzes physical layer features of received RF signals to identify drone signatures, which are matched against a known library of drone signatures. Multiple sensors can be used in combination to enhance detection reliability, with global decision-making performed at a fusion center or mesh network.

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An autonomous unmanned aerial vehicle (UAV) detecting system that uses a blimp equipped with cameras and sensors to monitor airspace and detect unauthorized UAVs. The system generates an image signature of detected UAVs and compares it to a database of authorized UAV signatures to determine unauthorized presence. The blimp can be controlled to maintain a desired altitude and position, and can generate an alarm when an unauthorized UAV is detected.

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Autonomous drone security for secure opening and closing of provider locations like stores. The system uses drones to monitor the location perimeter for threats, defects, or suspicious objects before staff enter or exit. Drones equipped with sensors fly predetermined routes around the building. They scan using ultra-wideband (UWB) and RFID to detect anomalies. If a threat like a package is found, the drone alerts staff. If defects like fallen branches are found, they are reported for repair. The drones can also be dispatched to gather additional data on potential threats like loiterers.

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Unmanned aerial vehicles (UAVs) are increasingly adopted to perform various military, civilian, and commercial tasks in recent years. To assure the reliability of UAVs during these tasks, anomaly detection plays an important role in today's UAV system. With the rapid development of AI hardware and algorithms, leveraging AI techniques has become a prevalent trend for UAV anomaly detection. While existing AI-enabled UAV anomaly detection schemes have been demonstrated to be promising, they also raise additional security concerns about the schemes themselves. In this paper, we perform a study to explore and analyze the potential vulnerabilities in state-of-the-art AI-enabled UAV anomaly detection designs. We first validate the existence of security vulnerability and then propose an iterative attack that can effectively exploit the vulnerability and bypass the anomaly detection. We demonstrate the effectiveness of our attack by evaluating it on a state-of-the-art UAV anomaly detection scheme, in which our attack is successfully launched without being detected. Based on the understanding ... Read More

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Radar system for detecting aircraft signatures like drones that provides robust detection in challenging environments like bad weather without needing costly multistatic radar arrays. The system has multiple radar clusters, each with co-located transmit and receive antennas. This confined structure improves resolution and coverage. The radar uses sequential or simultaneous signal broadcasting and reception. It detects motion with Doppler shifts and stationary objects with micro-Doppler analysis. Multiple chirp signals with ramps and frequencies avoid aliasing and improve resolution. Co-located antennas, sequential/simultaneous signals, and micro-Doppler analysis enable robust drone detection without multistatic arrays.

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A system for detecting UAVs and their operators from an aircraft, comprising an array of RF antennas that sequentially receive and compare the amplitudes of RF signals from the UAV, enabling the calculation of the UAV's direction of flight relative to the aircraft.

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A system for detecting and responding to unauthorized unmanned aerial vehicles (UAVs) in airspace. The system includes a surveillance drone that detects breaches of predetermined rules, such as no-fly zones, and communicates the breach to a control center. The control center can then instruct other UAVs to capture evidence of the unauthorized UAV or take action to disable it. The system enables safe and efficient management of UAV traffic in shared airspace.

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A method and apparatus for detecting and identifying unmanned aerial vehicles (UAVs) using radio wave measurement. The method generates a spectrogram, determines a region for direction finding, and finds the direction of the UAV based on signal values in that region. It further determines a region for UAV type identification and identifies the UAV type based on signal values in that region. The apparatus includes a processor that generates a spectrogram, determines regions for direction finding and type identification, and performs direction finding and type identification based on signal values in those regions.

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Recently, the number of drones has increased, and drones' illegal and malicious use has become prevalent. The dangerous and wasteful effects are substantial, and the probability of attacks is very high. Therefore, an anomaly detection and protection system are needed. This paper aims to design and implement an intelligent anomaly detection system for the security of unmanned aerial vehicles (UAVs)/drones. The proposed system is heavily based on utilizing ICMetric technology to exploit low-level device features for detection. This technology extracts the accelerometer and gyroscope sensors' bias to create a unique number known as the ICMetric number. Hence, ICMetric numbers represent additional features integrated into the dataset used to detect drones. This study performs the classification using a deep neural network (DNN). The experimental results prove that the proposed system achieves high levels of detection and performance metrics.

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Distributed airborne acoustic anti-drone intelligence system (DAAADS) that uses acoustic sensors on tethered balloons to detect, track, and predict trajectories of drones approaching a protected site. It alerts an air defense unit to intercept the drones, then tracks the debris to predict if any will hit the site. The system calculates drone speeds and uses regression to predict trajectories. It also uses machine learning to identify threats and debris.

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With the rapid development of unmanned aerial vehicle (UAV) technologies, UAVs are now increasingly leveraged to perform military and civilian tasks today. Meanwhile, as a complex cyber-physical system, UAVs are also facing security and reliability concerns raised by internal systems errors and external cyber-attacks from multiple aspects. Recent research has spent efforts on leveraging AI and machine learning techniques to predict the flying status of UAVs using their flight data for anomaly detection. However, these methods often ignore the prediction delay existing in status-changing periods during the UAVs operation, which inevitably causes false alarms and opens a window for malicious adversaries if they are not appropriately addressed. In this paper, we propose a new approach to enable effective anomaly detection and recovery for UAV flight data. Our approach adopts a hybrid design to eliminate false alarms during the status-changing periods while maintaining the high reliability of anomaly detection. We evaluate the proposed approach on flight data collected from multiple UAV... Read More

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The increasing use of Unmanned Aerial Vehicles (UAVs) in modern civilian and military applications shows the urgency of having a robust drone detector that detects unseen drone RF signals. Ideally, the system can also classify known RF signals from known drones. This study aims to develop an incremental-learning framework which can classify the known RF signals, and further detect novel RF signals. We propose DE-FEND: a Deep residual network-based autoEncoder FramEwork for known drone signal classification, Novelty Detection, and clustering. The known signal classification and novelty detection are performed in a semi-supervised and unsupervised manner, respectively. We used commercial drone RF signals to evaluate the performance of our framework. With our framework, we obtained 100% novelty detection accuracy at 1.04% False Alarm Rate (FAR) and 97.4% classification accuracy with only 10% labelled samples. Furthermore, we show that our framework outperforms the state-of-the-art (SoA) algorithms in terms of novelty detection performance.

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System for detecting, tracking, and managing unmanned aerial vehicles (UAVs) using a network of sensors and portable countermeasure devices. The system employs video, audio, Wi-Fi, and RF sensors to detect and identify UAVs, and a central processing unit to analyze sensor data and determine UAV locations. When a UAV is detected, the system transmits location information to nearby portable countermeasure devices, which can then generate and transmit disruptive signals to deter the UAV. The system also enables real-time tracking and monitoring of UAVs, and can store information on known UAVs to improve detection and management capabilities.

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With the rapid development of communication infrastructure and the widespread of IoT computing facilities such as Mobile Edge Computing (MEC), the application of Unmanned Aerial Vehicles (UAVs) are increasingly growing especially in last-mile deliveries in smart logistics. However, the anomaly detection in the MEC environment is still one yet unresolved issue. In recent years, the methods of UAV anomaly detection mainly focus on detections of specific types of UAVs or a certain feature of UAVs, and it is not as certain to achieve efficient and reliable results, in other words, there is currently a lack of better ways to address security issues in UAV networks. In this paper, we propose a Multi-Featured Anomaly Detection (MFAD) method for MEC-based UAV delivery systems to detect malicious attacks in the network through the abnormal state of UAV. Firstly, we develop deep learning model to recognize the correct UAV behaviors using several common UAV data in normal states, including altitude and speed. Secondly, we apply attacks in the data to simulate anomalous UAV behaviors under cyber... Read More

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The recent introduction of unmanned aerial vehicles (UAVs) sectors has proven to be highly promising in fields like agricultural, medical, industrial automation, remote monitoring, and military applications. For data collection from other linked Internet of Things (IoT)-based devices and for easing communication within the UAV network, the UAVs heavily rely on wireless communication protocols. They are vulnerable to attacks because of the issues brought on by remote operations and reliance on wireless protocols. In order to identify and isolate threats, it is important to design an intrusion detection system (IDS) for UAVs. In accordance with this perspective, the work presented in this article is concentrated on developing an intelligent framework for anomaly detection in UAV networks. The effectiveness of the suggested model with respect to various performance measures, such as precision, recall, F-measure, prediction accuracy, and CPU time, is empirically demonstrated using real-world UAV data. The proposed model was found to perform better than base models by offering an accuracy... Read More

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With the wide application of unmanned aerial systems, cyber security concerns of the system have attracted more and more attention. Accurate intrusion detection is an important prerequisite for the system to prevent and respond to cyber-attacks of the unmanned aerial system effectively. In this survey, the security issues are described firstly, and then several aspects of a general intrusion detection system such as detection mechanism, performance metrics are introduced. In particular, for unmanned aerial systems, their intrusion detection systems are categorized according to detection targets: modular intrusion detection, anomalous behavior monitoring in single UAV and malicious node identification in multiple UAVs. Finally, several research challenges and opportunities such as validation of intrusion detection system, anomaly-based detection are discussed.

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A security system that uses wireless signals to detect and track individuals or vehicles, with a sensor radio that monitors for unusual behavior and dynamically changes frequency or communicates with other radios when an anomaly is detected.

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The significant proliferation of the Internet of drone (IoD) network due to its enormous benefits in adverse terrains has become crucial in military operations, especially for swift aerial maneuvering and combat scenarios. However, military-based IoD networks are highly susceptible to lethal attacks as a result of severed network configuration. To mitigate these security issues, recent research is focused on designing intrusion detection systems (IDS) that monitor and analyze the telemetry data that flows via the participating nodes in the IoD, hence, impeding the invasion of any malicious attack launched at the network. In this study, a hierarchical and optimized random forest (RF) anomaly-based IDS is proposed based on the randomized search cross-validation (RSCV) technique; an optimization hyper-parameter algorithm, suitable for a delay-tolerant network such as the M-IoD with consideration of the payload constraints, battery constraints and high mobility of the drones in the network. The simulation results show the superiority of the proposed model achieving the highest F1-score o... Read More

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Method for determining whether a wireless device is airborne in a communication network, comprising obtaining measurement data for radio signals transmitted by base stations, and inputting the data to a machine learning model to classify the device as airborne or grounded.

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The synchronization of swarms of drones (also known as unmanned aerial vehicles (UAV)) in a network can be attributed to their high mobility and maneuverability capabilities, making them deployable for time-critical operations such as security surveillance, disaster management, and search and rescue operations. However, the resource constraints of these flying robots are limitations to their functionalities. Likewise, the neglect of the security status of this network significantly promotes attacks by invaders, thus, thwarting the mission of this network. In this study, mobile edge computing (MEC) technology and anomaly-based intrusion detection scheme are leveraged to curb these challenges using an optimized Random Forest (RCSV) model embedded in dedicated UAV-MEC servers. The selection of prominent features and hyperparameters for modeling an optimized attack predictor is enabled by Pearson correlation coefficient (PCC) and randomized search cross-validation techniques. Also, the training and evaluation of the proposed model were achieved using intrusion detection data set (CICIDS2... Read More

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System for detecting and disabling rogue unmanned aerial vehicles (UAVs) that attempt to hijack or interfere with other UAVs through wifi attacks. The system uses a sensor network of strategically located detectors to identify unauthorized UAVs and their frequencies. When a rogue UAV is detected, a response mechanism like a radio frequency jammer can be activated to disable the rogue UAV. This provides a physical countermeasure against unauthorized UAVs trying to take over or interfere with other UAVs.

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System and method for detecting and countering unauthorized unmanned aerial vehicles (UAVs) in a monitored airspace. The system uses detection, recognition, classification, and identification modules to determine if a detected object is a UAV. If the UAV is unidentified or unauthorized, a neutralization module suppresses the control signal to force it to leave the airspace. The system can integrate with existing security systems to protect against UAV intrusions without damaging them.

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Detecting and passively monitoring communications of an unmanned aerial vehicle (UAV) by scanning radio frequency spectra to identify modulated signals transmitted by the UAV, demodulating and decoding the signals to extract sensor data characteristics, and generating alerts based on predetermined thresholds or patterns in the data.

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A radar system that generates a three-dimensional data array from received radar data, comprising range, radial velocity, and beam line information. The system optimizes the data for classification by selecting and cropping the data array to focus on detected objects, and normalizing the intensity values. The processed data array is then input to a neural network for classification.

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A reconnaissance UAV for detecting unauthorized drones in a no-fly zone, comprising an image sensor and a noise sensor, and a surveillance flight method that dynamically changes sensor direction and position to detect small targets. The UAV flies a grid pattern across the no-fly zone, with each UAV monitoring a specific area and reporting back to a master UAV. The master UAV coordinates the flight pattern and sensor direction changes to ensure comprehensive coverage.

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A drone detection and tracking system using a phased array radar with an arc-shaped antenna configuration. The system comprises a modular hardware architecture with separate circuit boards for RF processing, signal processing, and digital processing. The radar system can be calibrated using a calibration process that involves transmitting a signal towards a single target and measuring phase offsets between transmitter and receiver pairs.

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Detecting and classifying unmanned aerial vehicles (UAVs) using RF signals, with improved performance at low signal-to-noise ratios. The system leverages spectrogram representations of RF signals to distinguish between UAVs and their controllers, and employs CNNs to classify and identify UAVs based on RF signal characteristics. The approach achieves higher classification accuracy than traditional methods, particularly at low signal-to-noise levels, by incorporating RF signal processing techniques. The system enables real-time threat assessment of UAVs through automated identification and classification, enabling regulatory bodies to implement effective security measures.

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