Figure 2 - uploaded by Mauro Leonardi
Content may be subject to copyright.
Source publication
Automatic Dependent Surveillance-Broadcast (ADS-B) is a surveillance system used in Air Traffic Control. With this system, the aircraft transmits their own information (identity, position, velocity, etc.) to any equipped listener for surveillance scope. The ADS-B is based on a very simple protocol and does not provide any kind of authentication and...
Contexts in source publication
Context 1
... message is composed of a preamble of four pulses and a data-block of 112 pulses where the information are coded with a 24-bit Cyclic Redundancy Check (CRC) [4,5]. Every message also contains a 24-bit unique identifier of the transponder (i.e., the unique identifier of the aircraft) called ICAO address [4]; in Figure 2 the format of the ADS-B message is reported. Various types of messages, with different data rates, can be coded and sent, such as: Aircraft Identification, Surface Position, Airborne Position (with Baro Altitude or with GPS Altitude), Airborne Velocities, etc. ...
Citations
... M. Leonardi et al. [21] proposed a detection model that detects malicious messages by fingerprinting wireless radio signals. Still, even though their proposed method is unfamiliar, only 50 percent of the malicious signals were detected. ...
... They achieved 90% accuracy in detecting spoofed messages and showed that their method could help distinguish the different ADS-B messages. • M. Leonardi et al. [21] proposed a detection model that detects malicious messages by fingerprinting wireless radio signals. Still, even though their proposed method is unfamiliar, only 50 percent of the malicious signals were detected. ...
... Analysis Algorithm/Method Accuracy F1-Score MCC [4] Multiple machine learning models 90% 92% - [21] Fingerprinting radio signals 50% -- Our results show that it is possible to build precise detection models for this kind of data that can operate across various stages of the life cycle of these messages by relying on the analysis of transmitted signals. The suggested detection model outperformed the majority of those in the literature in terms of results. ...
Automatic dependent surveillance-broadcast (ADS-B) is the future of aviation surveillance and traffic control, allowing different aircraft types to exchange information periodically. Despite this protocol’s advantages, it is vulnerable to flooding, denial of service, and injection attacks. In this paper, we decided to join the initiative of securing this protocol and propose an efficient detection method to help detect any exploitation attempts by injecting these messages with the wrong information. This paper focused mainly on three attacks: path modification, ghost aircraft injection, and velocity drift attacks. This paper aims to provide a revolutionary methodology that, even in the face of new attacks (zero-day attacks), can successfully detect injected messages. The main advantage was utilizing a recent dataset to create more reliable and adaptive training and testing materials, which were then preprocessed before using different machine learning algorithms to feasibly create the most accurate and time-efficient model. The best outcomes of the binary classification were obtained with 99.14% accuracy, an F1-score of 99.14%, and a Matthews correlation coefficient (MCC) of 0.982. At the same time, the best outcomes of the multiclass classification were obtained with 99.41% accuracy, an F1-score of 99.37%, and a Matthews correlation coefficient (MCC) of 0.988. Eventually, our best outcomes outdo existing models, but we believe the model would benefit from more testing of other types of attacks and a bigger dataset.
... In order to evaluate these methods, various wireless devices have been used, e.g., , Bluetooth [10][11][12][13][14], RFID [15], and internet-of-things (IoT) transmitters [16,17]. Moreover, RFF methods have also been used for aircraft identification based on ADS-B signals in several studies [18][19][20][21][22][23][24], where the efficiency of RFF in boosting the security of ADS-B systems has been proven. ...
The automatic dependent surveillance broadcast (ADS-B) system is one of the key components of the next generation air transportation system (NextGen). ADS-B messages are transmitted in unencrypted plain text. This, however, causes significant security vulnerabilities, leaving the system open to various types of wireless attacks. In particular, the attacks can be intensified by simple hardware, like a software-defined radio (SDR). In order to provide high security against such attacks, radio frequency fingerprinting (RFF) approaches offer reasonable solutions. In this study, an RFF method is proposed for aircraft identification based on ADS-B transmissions. Initially, 3480 ADS-B samples were collected by an SDR from eight aircrafts. The power spectral density (PSD) features were then extracted from the filtered and normalized samples. Furthermore, the support vector machine (SVM) with three kernels (linear, polynomial, and radial basis function) was used to identify the aircraft. Moreover, the classification accuracy was demonstrated via varying channel signal-to-noise ratio (SNR) levels (10–30 dB). With a minimum accuracy of 92% achieved at lower SNR levels (10 dB), the proposed method based on SVM with a polynomial kernel offers an acceptable performance. The promising performance achieved with even a small dataset also suggests that the proposed method is implementable in real-world applications.
... It is known that these properties depend on different factors, such as the stability of the oscillator, phase noise, and transmitter clock. For this reason, it is stated that there are differences between manufacturers, and aircraft can be classified using these differences [32]. Electromagnetic features containing more specific information than route information were extracted and used as RF fingerprints, and aircraft were classified according to these features. ...
... Electromagnetic features containing more specific information than route information were extracted and used as RF fingerprints, and aircraft were classified according to these features. In order to determine whether the aircraft are real or fake, a database that identifies unique aircraft with ICAO numbers must be created [32]. In [32], the authors stated that more than 50% of the observed planes have a specific phase order. ...
... In order to determine whether the aircraft are real or fake, a database that identifies unique aircraft with ICAO numbers must be created [32]. In [32], the authors stated that more than 50% of the observed planes have a specific phase order. International Civil Aviation Organization (ICAO) standards allow manufacturers to develop devices with system parameters within specific ranges. ...
This paper focuses on the vulnerabilities of ADS-B, one of the avionics systems, and the countermeasures taken against these vulnerabilities proposed in the literature. Anomaly detection methods based on machine learning and deep learning algorithms among proposed countermeasures against vulnerabilities of ADS-B are analyzed in detail. The advantages and disadvantages of using an anomaly detection system on ADS-B data are investigated. Thanks to advances in machine learning and deep learning in the last decade, it has become more appropriate to use anomaly detection systems to detect anomalies in ADS-B systems. To the best of our knowledge, this is the first survey focused on studies that use machine learning and deep learning algorithms for ADS-B security. In this context; this paper addresses research on this topic from different perspectives, and draws a road map for future research, and searches for five research questions related to machine learning and deep learning algorithms used on anomaly detection systems.
... Given that in the past few years RF fingerprint recognition methods have been successfully used in the automatic dependent surveillance-broadcast (ADS-B) system of air traffic control to identify aircraft [32], the experiment uses ADS-B system IQ signals from multiple aircraft as data support. There are a total of 25 classes of ADS-B signals. ...
Open set recognition (OSR) aims at dealing with unknown classes that are not included in the train set. However, existing OSR methods rely on deep learning networks that perform supervised learning on known classes in the train set, resulting in poor performance when the unknown class is very similar to the known class. Considering the subtle individual differences under the same type in specific emitter identification (SEI) applications, it is difficult to distinguish between known classes and unknown classes in open set scenarios. This paper proposes a pseudo signal generation and recognition neural network (PSGRNN) to address relevant problems in this situation. PSGRNN applies complex-value convolution operations to accommodate IQ signal inputs. Its key idea is to utilize samples of known classes to generate pseudo samples of unknown classes. Then, the samples of known classes and the generated pseudo samples of unknown classes are jointly input into the neural network to construct a new classification task for training. Moreover, the center loss is improved by adding inter-class penalties to maximize the inter-class difference. This helps to learn useful information for separating known and unknown classes, resulting in clearer decision boundaries between the known and the unknown. Extensive experiments on various benchmark signal datasets indicate that the proposed method achieves more accurate and robust open set classification results, with an average accuracy improvement of 4.62%.
... Reference [14] explored the RFFs of aircraft based on radar signals. Moreover, references [15][16][17] explored the RFFs of airplanes, targeting the ads-b s modal signal. The traditional ads-b signal represents the ads-b s mode, which is also used in this paper. ...
To improve communication stability, more wireless devices transmit multi-modal signals while operating. The term ‘modal’ refers to signal waveforms or signal types. This poses challenges to traditional specific emitter identification (SEI) systems, e.g., unknown modal signals require extra open-set mode identification; different modes require different radio frequency fingerprint (RFF) extractors and SEI classifiers; and it is hard to collect and label all signals. To address these issues, we propose an enhanced SEI system consisting of a universal RFF extractor, denoted as multiple synchrosqueezed wavelet transformation of energy unified (MSWTEu), and a new generative adversarial network for feature transferring (FTGAN). MSWTEu extracts uniform RFF features for different modal signals, FTGAN transfers different modal features to a recognized distribution in an unsupervised manner, and a novel training strategy is proposed to achieve emitter identification across multi-modal signals using a single clustering method. To evaluate the system, we built a hybrid dataset, which consists of multi-modal signals transmitted by various emitters, and built a complete civil air traffic control radar beacon system (ATCRBS) dataset for airplanes. The experiments show that our enhanced SEI system can resolve the SEI problems associated with crossing signal modes. It directly achieves 86% accuracy in cross-modal emitter identification using an unsupervised classifier, and simultaneously obtains 99% accuracy in open-set recognition of signal mode.
... Traditional signal feature extraction is based on the physical meaning of the signal itself, and frequently extracted features include phase features [2], frequency features [3], distances between signal constellation points [4], higher-order moments and higher-order accumulations [5,6], second-order and higher-order cyclostationarity [7,8] and wavelet decomposition features [9,10]. In addition, the researchers have also worked on exploring features that can characterize the unique properties of the signals, for example, In [11], variational mode decomposition is used for bearing fault diagnosis, and results show that the feature can eliminate signal noise and strengthen characteristics. ...
Feature engineering is a difficult task, and for real signal data, it is difficult to find a certain feature that can easily distinguish all classes. Multiple features can provide more information, which means the fusion of multi-feature learning strategies has potential significant advantages. Based on this premise, this paper proposes a multi-class framework based on the multi-featured decision to distinguish all the different classes, and takes Automatic Dependent Surveillance-Broadcast (ADS-B) signal data as an example, first extracts the phase features and wavelet decomposition features of the signal data, then selects the features with high discrimination between classes, then proposes a one-dimensional residual neural network based on 16 convolutional layers to learn the unique features of different features and classes separately, and finally proposes a novel multi-featured decision method based on voting method and a priori probability. Results show that the proposed one-dimensional residual neural network has better performance metrics on the test set compared to some machine learning-based and neural network-based algorithms, with classification accuracies of 86.1%, 84.6% and 83.6% on wavelet decomposition features, raw features and phase features, respectively, on ADS-B preamble signals. The proposed feature decision framework based on the voting method and a priori probability has a recall, precision and F1 value of 80.24%, 89.89% and 84.79% on ADS-B preamble signals, respectively.
... The complex baseband filtered signal y(t) can be written as [23] ...
... where z(t) is the Mode S trapezoidal transmit pulse defined as [23] ...
A new Sense and Avoid (SAA) method for safe navigation of small-sized UAVs within an airspace is proposed in this paper. The proposed method relies upon cooperation between the UAV and the surrounding transponder-equipped aviation obstacles. To do so, the aviation obstacles share their altitude and their identification code with the UAV by using a miniaturized Mode S operation Secondary surveillance radar (SSR) after interrogation. The proposed SAA algorithm removes the need for a primary radar and a clock synchronization since it relies on the estimate of the aviation obstacle's elevation angle for ranging. This results in more accurate ranging compared to the round-trip time-based ranging. We also propose a new radial velocity estimator for the Mode S operation of the SSR which is employed in the proposed SAA system. The root-mean-square error (RMSE) of the proposed estimators are analytically derived. Moreover, by considering the pulse-position modulation (PPM) of the transponder reply as a waveform of pulse radar with staggered multiple pulse repetition frequencies, the maximum unambiguous radial velocity is obtained. Given these estimated parameters, our proposed SAA method classifies the aviation obstacles into high-, medium-, and low-risk intruders. The output of the classifier enables the UAV to plan its path or maneuver for safe navigation accordingly. The effectiveness of the proposed estimators and the SAA method is confirmed through simulation experiments.
... Their main concern was to present the availability of such a low-cost attack setup and how it might motivate many attackers to perform malicious activity in the aviation industry. M. Leonardi et al. [23] proposed a detection model that detects malicious messages by fingerprinting wireless radio signals. Still, even though their proposed method is unfamiliar, only 50 percent of the malicious signals were detected. ...
Automatic Dependent Surveillance-Broadcast (ADS-B) is considered the future of aviation surveillance and traffic control as it allows different types of aircraft to transmit and gain information about their and other nearby aircraft's positions, velocity, and various other variables periodically. But, as this protocol still show that it lacks in terms of security and that researchers are still developing more methods and frameworks in order to secure this technology, we decided to join the initiative and propose an efficient detection method to help aid with detecting any attempts at injecting these messages which would cause multiple risks to aircrafts such as causing collision avoidance system failure, reporting wrong status of an aircraft, or even stealing it. This paper focused mainly on three different attacks; path modification, ghost aircraft injection, and velocity drift attacks. The dataset we utilized consisted of authentic messages captured from the OpenSky Network and generated injected messages using PyCharm. This study aims to provide a revolutionary methodology that, even in the face of new attacks (zero-day attacks), can successfully detect injected messages. The main advantage was utilizing a recent dataset to create more reliable and adaptive training and testing materials, which were then preprocessed before using different machine learning algorithms to feasibly create the most accurate and time-efficient model. The best outcomes of the binary classification were obtained with 99.14% accuracy, an F1-Score of 99.14%, and a Matthews' Correlation Coefficient (MCC) of 0.982. At the same time, the best outcomes of the multiclass classification were obtained with 99.41% accuracy, an F1-Score of 99.37%, and a Matthews' Correlation Coefficient (MCC) of 0.988. The dataset is thought to offer good outcomes, but the model still requires more testing and a bigger dataset, bearing in mind that the model still needs to be tested against other types of attacks.
... Automatic dependent surveillance-broadcast (ADS-B) is an air traffic surveillance technology which relies on aircraft broadcasting their identity, Global Navigation Satellite System (GNSS)-derived position and other information derived from onboard systems [1][2][3][4][5][6][7]. The information can be received by air traffic control ground stations for surveillance purposes or received by other aircraft to facilitate situational awareness and allow selfseparation. ...
Automatic dependent surveillance-broadcast (ADS-B) is a very important communication and surveillance technology in air traffic control (ATC). In the future, more and more satellites will carry out ADS-B technology to perform a global coverage. In order to make full use of the resources in the satellite, this paper proposes a solution for satellite three-axis attitude determination using the ADS-B receiver. The principle of ADS-B-based attitude determination is presented first. On this basis, ADS-B-based methods are employed to solve the problem. To achieve a higher attitude determination precision, gyro is combined with the ADS-B receiver using a multiplicative extended Kalman filter (MEKF). Finally, a simulation is carried out and the result is presented.
... SB methods extract frequency domain features, such as power spectrum density (PSD) [19], [20], Hilbert spectrum [13], and time-frequency statistics [21]. MB features including IQ offset [22], clock skew [23], [24], CFO [25], [26], sampling frequency offset [27], etc, can be extracted from the received baseband signal. During the classification process, the authenticator will first feed the extracted features to train the classifier and then infer the device identity. ...
Radio frequency fingerprinting (RFF) is a promising device authentication technique for securing the Internet of things. It exploits the intrinsic and unique hardware impairments of the transmitters for RF device identification. In real-world communication systems, hardware impairments across transmitters are subtle, which are difficult to model explicitly. Recently, due to the superior performance of deep learning (DL)-based classification models on real-world datasets, DL networks have been explored for RFF. Most existing DL-based RFF models use a single representation of radio signals as the input. Multi-channel input model can leverage information from different representations of radio signals and improve the identification accuracy of the RF fingerprint. In this work, we propose a novel multi-channel attentive feature fusion (McAFF) method for RFF. It utilizes multi-channel neural features extracted from multiple representations of radio signals, including IQ samples, carrier frequency offset, fast Fourier transform coefficients and short-time Fourier transform coefficients, for better RF fingerprint identification. The features extracted from different channels are fused adaptively using a shared attention module, where the weights of neural features from multiple channels are learned during training the McAFF model. In addition, we design a signal identification module using a convolution-based ResNeXt block to map the fused features to device identities. To evaluate the identification performance of the proposed method, we construct a WiFi dataset, named WFDI, using commercial WiFi end-devices as the transmitters and a Universal Software Radio Peripheral (USRP) as the receiver. ...
