Securing ADS-B data transmissions using blockchain: a comprehensive survey and analysis

Examining visitors’ spatiotemporal movement patterns within parks and protected areas (PPAs) has become an informative methodology for management actions and understanding visitor behavior. This work has given managers and researchers a better understanding of visitor spatial behaviors, spatiotemporal resource impacts, and theoretical and organizing frameworks for approaching this type of research. However, few studies have examined the spatial patterns of air traffic, specifically low-level air tours, above PPAs and the resulting impacts to visitors’ experience and the natural soundscapes. This need is particularly relevant in the U.S. due to legislative mandates that the National Park Service (NPS) and Federal Aviation Administration (FAA) have to manage air tours over NPS units. Further, prior studies have shown how air tours negatively impact both visitors’ experience and the natural soundscapes. Understanding the spatiotemporal patterns of air tours is critical for the managers working with air tour operators and the FAA for planning, ensuring compliance with management decisions, and examining the effects of aircraft noise on PPA resources and the on-the-ground visitors. Recently enacted laws and regulations across the globe, including the U.S., are requiring all aircraft operating in controlled airspace to equip air traffic tracking technology called Automatic Dependent Surveillance-Broadcast (ADS-B) Out avionics. ADS-B Out broadcasts an unencrypted and publicly accessible signal that relays latitude, longitude, altitude, and unique identification code. In the U.S., this code can then be cross-referenced to a publicly accessible database with information including the type/model of aircraft and the registered owner. Only a single prior study has examined ADS-B data over NPS units, finding both potential and limitations to this new technology for tracking air tours. This study seeks to expand methodological improvements and ADS-B’s potential to inform management actions by exploring ADS-B data at Haleakalā National Park (HALE). This study explores the use of ADS-B data at HALE for monitoring compliance with spatially explicit conditions within a preexisting agreement established between the park and air tour operators. The study identified 321 air tours, compared with 454 air tours reported by operators over the same time period. Compliance with the spatially explicit agreement conditions was generally high, though better for lateral offsets than minimum altitude requirements. Overall, this study advances methodological uses of ADS-B logging to track air tours over PPAs and advances knowledge about the potential utility and current limitations of using ADS-B tracking systems for compliance in PPA contexts. Subscribe to JPRA

Study of ADS-B Data Evaluation

One of the main values of utilizing automatic dependent surveillance - broadcast (ADS-B) data for air traffic control is that it provides greatly improved coverage as well as improved accuracy. As with all technology advances and deployments, especially in the National Airspace System (NAS) domain, there is a long transition time when the old and new systems must work together. The goal of the virtual radar is to provide ADS-B data in a way that completely emulates the characteristics of the current physical radars such that any automation system receiving the data will not have to be modified. Some of the issues that must be addressed include accuracy differences, conversion from cartesian GPS coordinates to polar range-azimuth coordinates, update frequency, providing a conversion of the of the ADS-B data to common digitizer (CD) beacon messages, generating the beacon real time quality control (BRTQC) and search real time quality control (SRTQC) messages as well as the radar status messages. Update frequency has unique issues to be resolved in the fact that the timing of the ADS-B reports may not correspond with the time that virtual radar beam may have hit the target. This issue of time registration will be investigated and the approaches to solving them discussed. The test and validation of virtual radars may require some new procedures and test tools to adequately insure the accuracy and correct operation. The benefits of virtual radars are exciting as they allow all of the benefits of ADS-B, improved coverage, and accuracy, and less maintenance without having to wait for upgraded automation systems.

In-Trail Procedures (ITP) is an application of Automatic Dependent Surveillance-Broadcast (ADS-B) that allows aircraft to change flight levels in areas where current non-radar separation standards would prevent desirable altitude changes. The ITP avionics receive ADS-B Out position reports from aircraft at intermediate flight levels that could be blocking the desired climb or descent maneuver. The most recent standard for ADS-B broadcast is RTCA/DO-260B [1]. This is also known as Version 2. Equipage is mandated by 2020 in the US for the same aircraft that carry transponders. Equipage is mandated in Europe by 2015 for forward fit aircraft and by 2017 for retrofit aircraft. Standards have been developed for the ADS-B data accuracy requirements for ITP [2, 3]. The standards are straightforward to apply to aircraft equipped with transponders certified to DO-260B [1]. The standards are less straightforward to apply for aircraft equipped with transponders certified to the earlier standards of ADS-B, which are known as Version 0 or Version 1 [4, 5]. This paper shows that crosschecking ADS-B data with Traffic Alert and Collision Avoidance System (TCAS) data is an effective mitigation technique for uncertain ADS-B horizontal position accuracy and integrity for legacy Version 0 and Version 1 ADS-B transponders.

Millions of small Unmanned Aerial Systems (sUAS), or drones weighing less than 55 pounds, will fly in urban airspaces within a decade. This is in addition to goals for increased Urban Air Mobility (UAM): larger transportation UAS and piloted air vehicles, including air taxi service and innovative aviation designs to revolutionize transportation. A significant problem is the lack of current management systems for this extremely large volume of air traffic, composed of heterogeneous drone shapes, sizes, and onboard equipment capabilities, flying adjacent to larger UAM vehicles in spaces previously unoccupied by General Aviation (GA). Robust and resilient detection, identification, localization, and tracking of sUAS sharing UAM airspace is complicated by urban factors such as non-transmitting, non-cooperative drones. Exacerbating these challenges, current technological limitations and vulnerabilities in Global Positioning System (GPS), Automatic Dependent Surveillance-Broadcast (ADS-B), and Inertial Navigation Systems (INS) can pose safety and security threats. To streamline a vision of safe and secure sUAS-UAM shared airspace, we propose Drone Net, a UAS Traffic Management (UTM) network of multi-modal ground and flight instruments. Drone Net is an architecture fusing a network of passive visual and acoustic sensor nodes with active Radio Detection and Ranging (radar) and Light Detection and Ranging (lidar) sensing methods for UTM, designed for integration with existing Air Traffic Control (ATC) and future UAM systems. The purpose of the Drone Net system is to evaluate use of Electro-Optical/Infrared (EO/IR) and acoustic arrays networked within an urban UAS operating region such as uncontrolled Class-G and waiver-granted Class-D airspace. The Drone Net approach combines detection, tracking, and localization estimation from radar, ADS-B, and EO/IR such that the design is robust to sensor errors, sample loss, and spoofing or other types of attacks. The system experimental design allows for emulation of corrupted ADS-B, GPS, and INS data on our flight platform including capability to communicate with ground radar and EO/IR to recover from flight instrument major and minor malfunctions such that a Drone Net cooperative UAS can be engineered to be fail-safe and fail-secure. We hypothesize that the Drone Net network of rooftop sensors, shown below, provides this conceptualized fail-safe, fail-secure property based upon the combination of self-localization with backup and confirming data from the ground sensor network. Further, with ground-system sensor fusion as well as UTM flight plan and registration information fusion, the overall system can manage small UAS that are both compliant and non-compliant alongside GA and UAM more safely than a single mode like ADS-B alone. To test our hypothesis, we envision two experiments this year. First, a test to confirm that we can emulate ADS-B, GPS, and INS sensor data corruption, leading to recovery using backup flight (lidar, EO/IR) or ground (radar, EO/IR) data to safely land an ALTA6 or other test UAS. Second, a series of Sense-and-Avoid (SAA) experiments between our test UAS and a tethered aerial obstacle. In the future, we will use this experience with fail-safe, fail-secure methods and software to further test more advanced scenarios between multiple UAS. The network of Drone Net instruments in a local area as well as regional and more global cloud-based networks will allow for heterogeneous information fusion and algorithm development for multi-sensor drone detection, classification, and identification with more accuracy than a single database or sensor system. The latent power of the Drone Net project is in its open-design ground and flight sensor network with data sharing capability to improve data mining and machine learning over time for analysis and security applications. More concrete applications for Drone Net include UTM integration with ATC and UAM, but also airspace safety and security for campus and public venues in general. Herein, we will present our experimental design and current test data from Embry-Riddle Aeronautical University in collaboration with University of Colorado Boulder toward completing the open specification for ground and flight instruments. We would additionally like to invite others to participate in growing the network and the data available for UTM, UAM, and GA shared airspace research.

A-SMGCS is an airport system that is used to improve the efficiency of using runways, apron and taxiways by maximizing runway capacities in all situations and weather while maintaining the required level of security. One of the parameters that becomes a requirement in the A-SMGCS system is the Probabillity of Target Report (PTR) whose value must be greater or equal to 95% in the area maneuver with update rate of the data one second [1]. Automatic Dependent SurveillanceBroadcast (ADS-B) is one of the cooperative sensors in the A-SMGCS system. The ADS-B sensor is a receiver that is used to capture ADS-B data from aircraft or vehicles equipped with ADS-B transmitters. The amount of ADSB data received by the ADS-B receiver and is needed for the A-SMGCS system is one of the factors that determine the Probability of Target Report (PTR). This paper will test three ADS-B receivers which will be selected for use as a cooperative sensor in the next A-SMGCS development. From the results of analysis and retrieval of data obtained by Radarcape ADS-B receiver more suitable for the development of the A-SMGCS system.

Aircraft passive positioning by receiving radio signals of Distance Measuring Equipment (DME) together with processing data from Automatic Dependent Surveillance Broadcast (ADS-B) about surrounding air traffic is represented in paper. Interrogation messages of other aircraft are used with ground DME network reply messages to calculate Time Difference of Arrival and for further passive aircraft coordinates estimation by hyperbolic navigation method. Communication message transmission by ADS-B is considered as a source of DME interrogators location for relative navigation approach. Accuracy assessment of hyperbolic positioning takes into account uncertainty areas of DME interrogators location. Accuracy analysis is performed with computer-based simulation with live air traffic data.

Automatic Dependent Surveillance-Broadcast (ADS-B) technology offers significant advantages in civil aviation applications, including high update rates, low construction and maintenance costs, and rich data content. However, its heavy dependence on the Global Navigation Satellite System (GNSS) renders it vulnerable to GPS interference, which can severely degrade data quality. To address this issue, it is necessary to optimize flight trajectories by incorporating feature values derived from ADS-B data. This paper proposes a flight path optimization method based on the ADS-B Navigation Integrity Category (NIC). First, features are extracted by analyzing the downlink data structure of ADS-B to filter out flight path points with low NIC values. Subsequently, radar data is dynamically integrated with the refined ADS-B data to enhance trajectory continuity during GPS interference events. Finally, the method is validated using real flight data, which also aids in the identification of GPS interference sources. The findings of this study can be applied to improve the quality of flight trajectories, trace the origins of GPS interference, optimize air traffic management efficiency, and significantly enhance aviation safety.

One of the disadvantages of the Automatic Dependent Surveillance-Broadcast (ADS-B) communications protocol is its vulnerability to a number of security attacks since it broadcasts information of the flying aircraft carrying the device over unencrypted datalink. As the U.S. Federal Aviation Administration has mandated the use of ADS-B by 2020, solid, reliable security solutions are needed to secure ADS-B data exchange. The first step in this regard is investigating ADS-B attacks and their impacts on air traffic. Therefore, in this paper, we describe a preliminary work performed to study some effects of an ADS-B attack category, message injection, using Hardware in the Loop testing platform. Using a set of flight plans developed to generate aircraft encounters, results show that this attack can distract pilots and ground controllers, cause air traffic disturbance, and increase the risk of aircraft collisions.

In this paper, we develop a novel method to detect Global Navigation Satellite Systems (GNSS) spoofing for an Automatic Dependent Surveillance-Broadcast (ADS-B) equipped aircraft. The Federal Aviation Administration (FAA) has mandated [18] all civil aircraft to be ADS-B Out equipped by January 1, 2020. The ADS-B Out broadcast sent to Air Traffic Control (ATC) consists of the aircraft's position, velocity, and other aircraft-specific information, all of which being unencrypted, poses a serious integrity threat. With readily available ADS-B trackers [19], [20], a spoofer can accurately track an aircraft to generate a spoofed trajectory that can go undetected [11]. We propose a novel method to modulate the ADS-B Out position broadcast such that a spoofed trajectory generated using the modulated ADS-B will be detectable by comparing Inertial Navigation System (INS) positions against those obtained using the spoofed GNSS signal. The amplitude of ADS-B modulation is selected to exceed the nominal INS error covariance so that spoofing is observable. In this work, we analytically quantify the magnitude of ADS-B modulation that will be sufficient for spoofing detection. During scenarios of GNSS signal jamming and spoofing, reauthentication of a reacquired GNSS signal is necessary to maintain integrity. During a GNSS outage, given enough time, the INS solution drift will grow large enough such that the spoofed GNSS solution might be within the INS error covariance envelope and go undetected. For GNSS outage scenarios we propose continuous modulation of ADS-B position and analytically quantify the magnitude such that spoofing of the GNSS signal is detectable after signal is re-acquired.

Automatic Dependent Surveillance-Broadcast (ADS-B) is an important part of the next generation air transportation systems, but its protocol does not provide relevant authentication and data encryption, so it is extremely vulnerable to various spoofing attacks. Therefore, it is necessary to develop a set of ADS-B data anomaly detection models. According to the strong temporal dependence and fast update rate of ADS-B data, we initiatively introduce a novel anomaly detection model based on Improved Generative Adversarial Network and long short-term memory networks (IGAN-LSTM). In the model, IGAN mainly improves the generator <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> architecture of GAN through upgrading the commonly used encoder architecture to encoder-decoder-encoder architecture, which captures the training data distribution within both data space and latent space. IGAN then utilizes encoding losses based on latent spatial features to determine whether a data sample is anomalous or not. At the same time, to model ADS-B data with the temporal dependence, we use the LSTM networks as the main architecture of the model, which is able to make use of long-term temporal dependence and avoid the vanishing gradient problem during training. Experimental results show that the proposed model has a higher area under the receiver operating characteristic curve and the precision recall curve than the other baseline methods from the literature, and reveal that the model has better performance of anomaly detection of ADS-B data.

Operations in the Next Generation Air Transportation System (NextGen) are planned to have a reduced dependency on primary surveillance radar and use Automatic Dependent Surveillance - Broadcast (ADS-B) to provide aircraft state information to Air Traffic Control (ATC) and onboard collision detection and avoidance systems. With the ongoing integration of Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS), ADS-B is expected to become the main surveillance input to UAS collision avoidance. Traffic Alert and Collision Avoidance System (TCAS) is unable to meet requirements in terms of adaptability to future operations with reduced separation margins, addition of new user groups (e.g., UAS), and support of additional surveillance inputs. Airborne Collision Avoidance System (ACAS X) is being designed to meet these requirements and to increase surveillance, alerting, and resolution performance. The RTCA has released Minimum Operational Performance Standards (MOPS) to provide system and operational performance characteristics that ADS-B must provide. This paper analyzes recorded ADS-B data from test flights that were performed in the Continental U.S. (CONUS) and verifies measured performance with specified operational characteristics in terms of coverage, accuracy, and integrity.

The commercial aviation industry mainly relies on ground-based radar system for air traffic surveillance. This traditional system has a fundamental shortcoming of limited coverage. The outcome of this limitation is expensive nonlinear airplane routing, inefficient utilization of air space, incidents like missing airplane and damaged black box that results in mystery about the cause of the accident, etc. In the recent decade, Automatic Dependent Surveillance-Broadcast (ADS-B) technology is considered for deployment by international aviation authorities as an advance alternate. The space-based ADS-B is recently considered more promising & useful than ground-based ADS-B. Owing to many advantages over the existing radar-based system, it is anticipated by the aviation industry to switch from radar-based system to ADS-B eventually. However, we have highlighted that even space-based ADS-B have some limitations that will prevent it from becoming a single surveillance system for ATC, and restricts it to be only additional support for the existing surveillance system. With the help of our recently proposed Space Air Ground Information Network (SAGIN), we have enlightened that utilization of space information network can resolve not only all these limitations but also aviation industry can be revolutionized.

The US Federal Aviation Administration is proposing an upgrade to air transportation that will fundamentally overhaul the current, aging system. A key component, the automatic dependent surveillance broadcast (ADS-B) system, will enhance air traffic monitoring and control by requiring aircraft to continually broadcast position, identity and velocity via unencrypted data links to ground stations. Although ADS-B may enhance air traffic safety and support the increase in traffic demands, open broadcast of clear aircraft data points raise serious security concerns. The ability to encrypt ADS-B message transactions would afford protection to ensure that the confidentiality of aircraft data is not compromised. The implementation of an encryption framework for a large, distributed and dynamic system, however, is nontrivial. This paper examines encryption schemes and highlights challenges associated with implementing confidentiality security protections for the ADS-B environment.

The paper studies instantaneous Doppler signature extraction from within a very high frequency (VHF) band spectrogram presented by the authors in previous work. The context of the current method is long-range aircraft detection by VHF Doppler effect. The method proposed calculates bistatic radar (BR) cross-section (BRCS) profiles and the correlation between them for different types of aircraft. The analysis is based on data represented by automatic dependent surveillance-broadcast (ADS-B) trajectory collection and passive BR with TV station as an illuminator of opportunity. Throughout the analysis, ADS-B data on location of an aircraft were adjusted with the use of extracted Doppler shift information. Then, this ground truth information on location was used for proper evaluation of BRCS profiles and, finally, for validation of the extraction method. The method is able to classify common intercontinental aircraft by size class with 70% accuracy from a 100-km distance by using an illuminator of opportunity located 300 km away.