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The surveillance sensor that has been mainly used for target tracking in air traffic control (ATC) environment is radar. The automatic dependent surveillance – broadcasting (ADS-B), which is based on the technologies of global navigation satellite systems, is recently participating in ATC systems. Although ADS-B provides more accurate measurements...
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... To enhance the security of ADS-B data, lots of security methods are put forward such as signal feature analysis [11][12][13][14][15], data fusion with SSR [16][17][18][19], law of motion validation [20], data encryption [21,22], digital signature [23][24][25], and so on. Generally, these methods rely on attack data to evaluate their efficiency and accuracy. ...
With the fast increase in airspace density and high‐safety requirements on aviation, automatic dependent surveillance‐broadcast (ADS‐B) is regarded as the primary method in the next generation air traffic surveillance. The ADS‐B data is broadcast with the plain text without sufficient security measures, which results in various attack patterns emerging. However, in terms of constrictions with laws and regulations, ADS‐B attack data is difficult to collect and obtain, which is essential for data security research studies. To deal with the absence of ADS‐B attack data in real environments, the construction strategy on ADS‐B attack data is proposed. For construction fidelity, ADS‐B data features are analysed and modelled at first. Then the popular and classical attack patterns on ADS‐B data are analysed to establish threat models. Based on the original ADS‐B data sets, the construction strategy is designed to focus on attack target selection, key parameter determination, and mixture strategy, reproducing the attack intentions. The constructed ADS‐B attack data sets are hybrid data sets including the normal and attack data. By simulation analyses, the feasibility and availability of the construction strategy were validated with real ADS‐B data.
... At present, the attack detection on ADS-B data is focused due to the mandated deployment deadline (2020 year) coming. Generally, the attack detection on ADS-B data mainly takes advantage of the motion laws [24] and third reference data [25]. On the one hand, the ADS-B data is validated with motion laws. ...
With the requirements on accuracy, coverage and reliability, the air traffic surveillance is being developed into the next generation. In 2020, ADS-B data is becoming the foundation to establish air traffic situation awareness capabilities. However, ADS-B is designed without sufficient security guarantees, which results in diverse attack threats. Hence, it is in demand of effective attack detections to keep attack data away from decision flows. To improve the accuracy and robustness, attack detection based on generative adversarial network for ADS-B data is proposed. The LSTM networks are the core components to set up the generator and discriminator to make the most of temporal spatial correlations. Utilizing the reconstruction error and discriminative loss, the comprehensive detection metric is obtained to identify attack behaviors. To enhance the robustness, the analysis threshold for detection decision is determined in terms of normal data intrinsic features. By experimental analysis on real ADS-B data, the accuracy and robustness of the proposed method is validated.
... Data fusion is also a vital way to enhance detection schemes. At present, SSR data ( Jeon et al., 2015 ) and MLAT data ( Márcio, 2015 ) can be adopted to accomplish cross check with ADS-B data, detecting large deviations and injected ghost flights. The detection methods are good at identifying fake injected messages whereas relies on the third reference data sources. ...
In the next generation air traffic surveillance, ADS-B is the primary surveillance method to improve situ- ation awareness capabilities. But ADS-B protocol is absent of sufficient security considerations, especially for data integrity and authentication. As a result, attack patterns on ADS-B data are emerging and effi- cient attack detection strategies are in great demand to enhance data security. To decrease the time delay of detection, enhance accuracy and mitigate the concept drift impacts, the online sequential attack detec- tion strategy based on hierarchical temporal memory are proposed. By applying binary encoding, ADS-B data is transformed into sparse distribution representation with temporal and spatial correlations. The encoded data is push into hierarchical temporal memory and online learning schemes are established for the ADS-B stream data. With the sequential ADS-B data, hierarchical temporal memory is updated and used to generate the deviations between predictions and original values for the corresponding ADS- B data. Designing and applying deviation analysis, sequential analysis and adaptive threshold check, the differences between normal and novelty are magnified and easy to be distinguished. According to exper- imental analysis, the attack detection strategy is efficient on processing time and accuracy.
... Due to the differences on sampling frequency, data coordination, time synchronization, target association and location accuracy, detection based on data fusion of two types is faced of challenges. Jeon [11] discussed coordination transformation, time synchronization, target association for SSR and ADS-B, and proposed validation methods based on Gaussian distribution hypothesis test. Zhang et al. [12] used Probability Hypothesis Density (PHD) filter to analyze the statistical differences between ADS-B data and SSR data, which can implement data attack detection without target associations. ...
Automatic Dependent Surveillance - Broadcast (ADS-B) surveillance is regarded as the core technology in the next generation air traffic management. Due to the absence of consideration on security, ADS-B data is faced with various challenges on integrity and authentication, especially for ADS-B data attack with high concealment. In this paper, common attack pattern models are analyzed. In terms of sequential ADS-B data, detection methods are designed according to flight and ground station capabilities, which integrate several detection methods, including flight plan validation, single node data detection and group data detection, to generate comprehensive attack probability as reference for judgment on data attack. To improve the positive detection ratio, ground to ground, ground to air and air to air collaborative detections are proposed to enhance each single node detection ability. Experiments conducted on real ADS-B data illustrated that the sequential collaborative detection strategy was efficient on effectiveness and accuracy, especially for random deviation injection attack, constant deviation injection attack and DoS attack.
... On the other hand, air operations are one of the human activities more sensitive to weather condition and many of the Communications, Navigation and Surveillance systems for Air Traffic Management (CNS-ATM) are obsolete according to studies developed by Finke Ahlstrom and Jeon [11]- [13] because they do not have automatic analysis and supporting systems for meteorological data. According to Rillo [9] this is one of the main causes of air accidents. ...
... Thus, they are working in NEXTGEN and SESAR [15]- [18] to improve air operational control. These are programs that involve the use of intelligent systems to improve the air surveillance and allow to integrate other information like temporal and geospatial identification of weather formations [15], [16], [19], [13], that could affect the CNS-ATM system. Through this system, they try to reduce risks by allowing air traffic controllers and pilots to make decisions based on integrated information from air surveillance and meteorological sensors. ...
En la aviación, los fenómenos meteorológicos son uno de los aspectos más importantes para tener en cuenta en todas las etapas de vuelo, desde la planificación hasta el aterrizaje. El desarrollo de sistemas de predicción meteorológica aplicados a la aviación puede apoyar el proceso de toma de decisiones de los controladores de tráfico aéreo y los pilotos, facilitando el análisis de las variables meteorológicas y proporcionando una primera interpretación a disposición de todos los usuarios del sistema aéreo.
Por esta razón el Centro de desarrollo Tecnológico para la Defensa (CETAD) tiene como principal objetivo en este documento describir los resultados del desarrollo de una metodología sistematizada que utiliza técnicas inteligentes para la detección, identificación y seguimiento de cualquier tipo de formación que por sus características pueda representar un riesgo para la aviación, generando a su vez información de soporte al controlador aéreo.
Para esto es necesario primero identificar las formaciones convectivas, clasificarlas, filtrar el ruido e individualizarlas. Este tipo de procesos pueden ser automatizados a través del análisis inteligente de productos disponibles en cualquier sistema aéreo como las imágenes satelitales multiespectrales.
Posterior a una identificación, se deben determinar un grupo de características que permitan desarrollar algoritmos eficientes capaces de realizar un seguimiento del comportamiento de la formación convectiva, que permita generar pronósticos de las características de los sistemas convectivos en el corto plazo, para lo que se requiere conocer otras variables como el viento en las áreas de análisis.
Este tipo de aplicaciones integradas a los sistemas de control de tráfico aérea disminuirían los riesgos debidos factores meteorológicos.
... Tu na scenu stupaju cloud servisi kao revolucija u nadzoru velikih i kompleksnih sistema za video nadzor o ĉemu će detaljnije biti opisano u ovom radu. Dostupna literatura se bavi ovim nedostacima i tehnologijama za podršku i neka od najznaĉajnih istraţivanja ukljuĉuju: sintezu podataka iz aplikacija za video nadzor[3,10], eliminaciju oštećenih frejmova iz snimka korišćenjem histograma[8], poboljšanje kvaliteta servisa u mobilnom cloudu za video nadzor[9], prikupljanje saobraćajnih podataka korišćenjem Bluetooth tehnologije[21], uklanjanje senke vozila u svrhu spreĉavanja laţne detekcije[22,42], integracije radarske i cloud tehnologije u vazdušnoj prismotri saobraćaja[26], procena stanja na putevima klasterskom analizom[30], softverska kalibracija sistema za video nadzor[43]. ...
Implementacija sistema za video nadzor omogućava monitoring i upravljanje saobraćaja kako bi se povećala bezbednost i sigurnost svih uĉesnika u saobraćaju. Takav sistem pruţa informacije u realnom vremenu o toku saobraćaja, zagušenjima i bilo kakvim potencijalnim bezbednosnim rizicima na razliĉitim delovima puta. Moderni sistemi imaju mogućnost kontrole semaforske signalizacije i preusmeravanju vozaĉa na alternativne pravce kako bi se optimizovao tok saobraćaja. Uzevši u obzir zahtevnost takvih operacija neophodni su ogromni raĉunarski resursi za njihovu obradu u realnom vremenu što se danas moţe ostvariti samo kroz korišćenje servisa cloud raĉunarstva i propratnih tehnologija. U radu je dat pregled postojećih informaciono-komunikacionih rešenja za video nadzor (VSaaS) saobraćaja korišćenjem cloud tehnologije kao i predlozi za njihovu implementaciju.
How to cite this article:
Dašić, P.; Dašić, J. & Crvenković, B.: Primena video nadzora kao cloud servisa (VSaaS) u oblasti saobraćaja. Rad po pozivu (Invitation Paper). U: Zborniku radova XV Međunarodnog savjetovanja "Saobraćajni, ekološki i ekonomski problemi i perspektive rješavanja u zemljama Zapadnog Balkana s osvrtom na Bosnu i Hercegovinu"; Vlašić, Bosna i Hercegovina (BiH); 19-20. maja 2017. Travnik (Bosna i Hercegovina): Internacionalni Univerzitet Travnik (IUT), 2017, pp. 80-95. ISSN 2232-8807.
Decision support systems serve as support in situations, contexts, or unstructured problems, such as the recognition and classification of patterns and the detection of anomalies or atopic data, through of process of mining modeling and analysis of data. This study shows an approach that uses artificial intelligence techniques and statistical descriptions applied to portions of the airspace that are delimited using Voronoi regions, with the purpose of perform automation of the air route validation process. To this end a systemic methodology compares data generated by aerial surveillance systems with historical data from the air traffic and control system of aircraft operating over Colombian air space. For the characterization of the routes, the mathematical properties of the Voronoi regions segment the airspace, which allows associating the detections that make up the trajectories of the aircraft to regions. The aggregation of points in each region makes it possible to describe the routes with a reduced number of characteristics that serve as input to supervised algorithms that classify the route to which each trajectory belongs with an accuracy greater than 95%. As described in this study, these types of validations are used by command-and-control systems as a basis for supporting the decision-making process.
Tracking multiple time-varying states based on heterogeneous observations is a key problem in many applications. Here, we develop a statistical model and algorithm for tracking an unknown number of targets based on the probabilistic fusion of observations from two classes of data sources. The first class, referred to as target-independent perception systems (TIPSs), consists of sensors that periodically produce noisy measurements of targets without requiring target cooperation. The second class, referred to as target-dependent reporting systems (TDRSs), relies on cooperative targets that report noisy measurements of their state and their identity. We present a joint TIPS-TDRS observation model that accounts for observation-origin uncertainty, missed detections, false alarms, and asynchronicity. We then establish a factor graph that represents this observation model along with a state evolution model including target identities. Finally, by executing the sum-product algorithm on that factor graph, we obtain a scalable multitarget tracking algorithm with inherent TIPS-TDRS fusion. The performance of the proposed algorithm is evaluated using simulated data as well as real data from a maritime surveillance experiment.
