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Volume 47 Issue 5
May  2025
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WANG Buhong, LUO Peng, YANG Yong, ZHAO Zhengyang, DONG Ruochen, GUAN Yongjian. A Review and Prospect of Cybersecurity Research on Air Traffic Management Systems[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1230-1265. doi: 10.11999/JEIT240966
Citation: WANG Buhong, LUO Peng, YANG Yong, ZHAO Zhengyang, DONG Ruochen, GUAN Yongjian. A Review and Prospect of Cybersecurity Research on Air Traffic Management Systems[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1230-1265. doi: 10.11999/JEIT240966

A Review and Prospect of Cybersecurity Research on Air Traffic Management Systems

doi: 10.11999/JEIT240966 cstr: 32379.14.JEIT240966
Funds:  The National Natural Science Foundation of China (62472437)
  • Received Date: 2024-10-29
  • Rev Recd Date: 2025-02-20
  • Available Online: 2025-03-08
  • Publish Date: 2025-05-01
  •   Significance   The air traffic management system is a critical national infrastructure that impacts both aerospace security and the safety of lives and property. With the widespread adoption of information, networking, and intelligent technologies, the modern air traffic management system has evolved into a space-air-ground-sea integrated network, incorporating heterogeneous systems and multiple stakeholders. The network security of the system can no longer be effectively ensured by device redundancy, physical isolation, security by obscurity, or human-in-the-loop strategies. Due to the stringent requirements for aviation airworthiness certification, the implementation of new cybersecurity technologies is often delayed. New types of cyberattacks, such as advanced persistent threats and supply chain attacks, are increasingly prevalent. Vulnerabilities in both hardware and software, particularly in embedded systems and industrial control systems, are continually being exposed, widening the attack surface and increasing the number of potential attack vectors. Cyberattack incidents are frequent, and the network security situation remains critical.   Progress   The United States’ Next Generation Air Transportation System (NextGen), the European Commission’s Single European Sky Air Traffic Management Research (SESAR), and the Civil Aviation Administration of China have prioritized cybersecurity in their development plans for next-generation air transportation systems. Several countries and organizations, including the United States, Japan, China, the European Union, and Germany, have established frameworks for the information security of air traffic management systems. Although network and information security for air traffic management systems is gaining attention, many countries prioritize operational safety over cybersecurity concerns. Existing security specifications and industry standards are limited in addressing network and information security. Most of them focus on top-level design and strategic directions, with insufficient attention to fundamental theories, core technologies, and key methodologies. Current review literature lacks a comprehensive assessment of assets within air traffic management systems, often focusing only on specific components such as aircraft or airports. Furthermore, research on aviation information security mainly addresses traditional concerns, without fully considering the intelligent and dynamic security challenges facing next-generation air transportation systems.   Conclusions   This paper comprehensively examines the complexity of the cybersecurity ecosystem in air traffic management systems, considering various entities such as electronic-enabled aircraft, communication, navigation, Surveillance/Air Traffic Management (CNS/ATM), smart airports, and intelligent computing. It focuses on asset categorization, information flow, threat analysis, attack modeling, and defense mechanisms, integrating dynamic flight phases to systematically review the current state of cybersecurity in air traffic management systems. Several scientific issues are identified that must be addressed in constructing a secure ecological framework for air traffic management. Based on the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) model, this paper analyzes typical attack examples related to the four ecological entities (Figs. 7, 9, 12, and 14) and constructs an ATT&CK matrix for air traffic management systems (Fig. 15). Additionally, with the intelligent development goal of next-generation air transportation systems as a guide, ten typical applications of intelligent air traffic management are outlined (Fig. 13, Table 11), with a systematic analysis of the attack patterns and defense mechanisms of their intelligent algorithms (Tables 12, 13). These findings provide theoretical references for the development of smart civil aviation and the assurance of cybersecurity in China.   Prospects   Currently, the cybersecurity ecosystem of air traffic management systems is highly complex, with unclear mechanisms, indistinct boundaries for cybersecurity assets, and incomplete security assurance requirements. Moreover, there is a lack of comprehensive, systematic, and holistic cybersecurity design and defense mechanisms, which limits the ability to counter various subjective, human-driven, and emerging types of malicious cyberattacks. This paper highlights key research challenges in areas such as dynamic cybersecurity analysis, attack impact propagation modeling, human-in-the-loop cybersecurity analysis, and distributed intrusion detection systems. Cybersecurity analysis of air traffic management systems should be conducted within the dynamic operational environment of a space-air-ground-sea integrated network, accounting for the cybersecurity ecosystem and analyzing it across different spatial and temporal dimensions. As aircraft are cyber-physical systems, cybersecurity threat analysis should focus on the interrelated propagation mechanisms between security and safety, as well as their cascading failure models. Furthermore, humans serve as the last line of defense in cybersecurity. When performing threat modeling and risk assessment for avionics systems, it is crucial to fully incorporate “human-in-the-loop” characteristics to derive comprehensive and objective conclusions. Finally, the design, testing, certification, and updating of civil aviation avionics systems are constrained by strict airworthiness requirements, preventing the rapid implementation of advanced cybersecurity technologies. Distributed anomaly detection systems, however, currently represent an effective technical approach for combating cyberattacks in air traffic management systems.
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  • [1]
    ZHANG Jun. Aeronautical mobile communication: The evolution from narrowband to broadband[J]. Engineering, 2021, 7(4): 431–434. doi: 10.1016/j.eng.2021.02.002.
    [2]
    POST J. The next generation air transportation system of the united states: Vision, accomplishments, and future directions[J]. Engineering, 2021, 7(4): 427–430. doi: 10.1016/j.eng.2020.05.026.
    [3]
    BOLIĆ T and RAVENHILL P. SESAR: The past, present, and future of European air traffic management research[J]. Engineering, 2021, 7(4): 448–451. doi: 10.1016/j.eng.2020.08.023.
    [4]
    中国民用航空局. 智慧民航建设路线图[R]. 2022.

    Civil Aviation Administration of China. Roadmap for building intelligent civil aviation[R]. 2022.
    [5]
    International Civil Aviation Organization. Global air traffic management operational concept[R]. Doc 9854, 2005.
    [6]
    马兰, 孟诗君, 吴志军. 基于BERT与生成对抗的民航陆空通话意图挖掘[J]. 系统工程与电子技术, 2024, 46(2): 740–750. doi: 10.12305/j.issn.1001-506X.2024.02.38.

    MA Lan, MENG Shijun, and WU Zhijun. Intention mining for civil aviation radiotelephony communication based on BERT and generative adversarial[J]. Systems Engineering and Electronics, 2024, 46(2): 740–750. doi: 10.12305/j.issn.1001-506X.2024.02.38.
    [7]
    WU Zhijun, LIANG Cheng, and ZHANG Yuan. Blockchain-based authentication of GNSS civil navigation message[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(4): 4380–4392. doi: 10.1109/TAES.2023.3241041.
    [8]
    KHAN H A, KHAN H, GHAFOOR S, et al. A survey on security of Automatic Dependent Surveillance -Broadcast (ADS-B) protocol: Challenges, potential solutions and future directions[J]. IEEE Communications Surveys & Tutorials, 2024.
    [9]
    NÖHREN L, SCHAPER M, and TYBURZY L. Towards full ATC automation for aircraft ground movement: A first step[C]. 2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC), San Diego, USA, 2024: 1–9. doi: 10.1109/DASC62030.2024.10749019.
    [10]
    ENAYATOLLAHI F, ATASHGAH M A A, MALAEK S M B, et al. PBN-based time-optimal terminal air traffic control using cellular automata[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(3): 1513–1523. doi: 10.1109/TAES.2020.3048787.
    [11]
    SAMPIGETHAYA K. Aircraft cyber security risk assessment: Bringing air traffic control and cyber-physical security to the forefront[C]. The AIAA Scitech 2019 Forum, San Diego, USA, 2019: 1–17. doi: 10.2514/6.2019-0061.
    [12]
    BOGODA L, MO J, and BIL C. A systems engineering approach to appraise cybersecurity risks of CNS/ATM and avionics systems[C]. 2019 Integrated Communications, Navigation and Surveillance Conference (ICNS), Herndon, USA, 2019: 1–15. doi: 10.1109/ICNSURV.2019.8735376.
    [13]
    MEHAN D J. Information systems security: The federal aviation administration’s layered approach[R]. 2000.
    [14]
    KIESLING T and KREUZER M. Recommendations to strengthen the cyber resilience of the air traffic system[EB/OL]. http://lb-campus.de/images/content/ARIEL_Recommendations_v2.0.pdf, 2017.
    [15]
    CSFI. CSFI ATC (Air Traffic Control) cyber security project[EB/OL]. https://csfi.us/pubdocs/?id=47, 2015.
    [16]
    European Commission. Global ATM security management project[EB/OL]. https://www.gamma-project.eu/, 2024.
    [17]
    Collaborative Actions for Renovation of Air Traffic Systems. Long-term vision for the future air traffic systems ~ changes to intelligent air traffic systems ~[EB/OL]. https://www.mlit.go.jp/koku/carats/rdk40188/wp-content/uploads/2020/06/LongTermVisionForTheFutureAirTrafficSystemsCARATS.pdf, 2010.
    [18]
    中国民用航空局. MH/T 0035-2012 民用航空网络与信息安全管理规范[S]. 北京: 中国民用航空局, 2012.

    Civil Aviation Administration of China. MH/T 0035-2012 Specification for civil aviation network and information security management[S]. Beijing: Civil Aviation Administration of China, 2012.
    [19]
    中国民用航空局. MH/T 0067-2018 民航Web应用系统安全检查指南[S]. 北京: 中国民用航空局, 2018.

    Civil Aviation Administration of China. MH/T 0067-2018 Security testing guide for Web application system of civil aviation[S]. Beijing: Civil Aviation Administration of China, 2018.
    [20]
    中国民用航空局. MH/T 0068-2018 民用航空移动应用程序安全测评指南[S]. 北京: 中国民用航空局, 2018.

    Civil Aviation Administration of China. MH/T 0068-2018 Security testing guide for mobile application program of civil aviation[S]. Beijing: Civil Aviation Administration of China, 2018.
    [21]
    中国民用航空局. MH/T 0069-2018 民用航空网络安全等级保护定级指南[S]. 北京: 中国民用航空局, 2018.

    Civil Aviation Administration of China. MH/T 0069-2018 Guidelines for grading of classified cyber security protection in civil aviation[S]. Beijing: Civil Aviation Administration of China, 2018.
    [22]
    中国民用航空局. MH/T 0076-2020 民用航空网络安全等级保护基本要求[S]. 北京: 中国民用航空局, 2020.

    Civil Aviation Administration of China. MH/T 0076-2020 Baseline for classified protection of cybersecurity in civil aviation[S]. Beijing: Civil Aviation Administration of China, 2020.
    [23]
    PIK E. Airport security: The impact of AI on safety, efficiency, and the passenger experience[J]. Journal of Transportation Security, 2024, 17: 9. doi: 10.1007/S12198-024-00276-6.
    [24]
    LYKOU G, ANAGNOSTOPOULOU A, and GRITZALIS D. Smart airport cybersecurity: Threat mitigation and cyber resilience controls[J]. Sensors, 2019, 19(1): 19. doi: 10.3390/s19010019.
    [25]
    LYKOU G, ANAGNOSTOPOULOU A, and GRITZALIS D. Implementing cyber-security measures in airports to improve cyber-resilience[C]. 2018 Global Internet of Things Summit (GIoTS), Bilbao, Spain, 2018: 1–6. doi: 10.1109/GIOTS.2018.8534523.
    [26]
    KORONIOTIS N, MOUSTAFA N, SCHILIRO F, et al. A holistic review of cybersecurity and reliability perspectives in smart airports[J]. IEEE Access, 2020, 8: 209802–209834. doi: 10.1109/ACCESS.2020.3036728.
    [27]
    HABLER E, BITTON R, and SHABTAI A. Assessing aircraft security: A comprehensive survey and methodology for evaluation[J]. ACM Computing Surveys, 2024, 56(4): 96. doi: 10.1145/3610772.
    [28]
    FAKEEH K A. An analysis of airports cyber-security[J]. Communications on Applied Electronics, 2016, 4(7): 11–15. doi: 10.5120/cae2016652129.
    [29]
    European Union Agency for Network and Information Security. Securing smart airports[R]. 2016.
    [30]
    DAVE G, CHOUDHARY G, SIHAG V, et al. Cyber security challenges in aviation communication, navigation, and surveillance[J]. Computers & Security, 2022, 112: 102516. doi: 10.1016/j.cose.2021.102516.
    [31]
    VILLEGAS J, FORTES S, ESCAÑO V, et al. Verification and validation framework for AFDX avionics networks[J]. IEEE Access, 2022, 10: 66743–66756. doi: 10.1109/ACCESS.2022.3184329.
    [32]
    张双, 孔德岐, 王元勋, 等. 基于虚拟化航电平台的网络域间安全通信技术[J]. 西北工业大学学报, 2022, 40(3): 530–537. doi: 10.1051/jnwpu/20224030530.

    ZHANG Shuang, KONG Deqi, WANG Yuanxun, et al. Secure communication technology between network domains based on virtualization avionics platform[J]. Journal of Northwestern Polytechnical University, 2022, 40(3): 530–537. doi: 10.1051/jnwpu/20224030530.
    [33]
    HINTZE H, GIERTZSCH F, and GOD R. Design approach for secure networks to introduce data analytics within the aircraft cabin[J]. SAE International Journal of Advances and Current Practices in Mobility, 2019, 2(2): 737–746. doi: 10.4271/2019-01-1853.
    [34]
    郭鹏军, 张泾周, 杨远帆, 等. 飞机机内无线通信网络架构与接入控制算法研究[J]. 计算机科学, 2022, 49(9): 268–274. doi: 10.11896/jsjkx.210700220.

    GUO Pengjun, ZHANG Jingzhou, YANG Yuanfan, et al. Study on wireless communication network architecture and access control algorithm in aircraft[J]. Computer Science, 2022, 49(9): 268–274. doi: 10.11896/jsjkx.210700220.
    [35]
    WERTHWEIN M, BRUNNER M, and ANNIGHOEFER B. A concept enabling cybersecurity for a self-adaptive avionics platform with respect to RTCA DO-326 and RTCA DO-356[C]. 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), Barcelona, Spain, 2023: 1–10. doi: 10.1109/DASC58513.2023.10311289.
    [36]
    SHAIKH F, RAHOUTI M, GHANI N, et al. A review of recent advances and security challenges in emerging E-enabled aircraft systems[J]. IEEE Access, 2019, 7: 63164–63180. doi: 10.1109/ACCESS.2019.2916617.
    [37]
    WU Zhijun, YOU Zhenghang, and WANG Peng. Attribute encryption based access control methods under airborne networks[C]. 2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), Melbourne, Australia, 2022: 302–306. doi: 10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00045.
    [38]
    PREDESCU A V and STELKENS-KOBSCH T H. Aviation Security Lab: A testbed for security testing of current and future aviation technologies[C]. 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), Portsmouth, USA, 2022: 1–5. doi: 10.1109/DASC55683.2022.9925750.
    [39]
    ŠEGVIĆ M, NIKOLIĆ K K, and IVANJKO E. A proposal for a Fully Distributed Flight Control System design[C]. 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 2016: 1099–1103. doi: 10.1109/MIPRO.2016.7522304.
    [40]
    WIELAND F and KILBOURNE T. A digital twin of a flight management system: Findings from the cloud FMS project[C]. The AIAA SCITECH 2024 Forum, Orlando, USA, 2024: 0278. doi: 10.2514/6.2024-0278.
    [41]
    HUANG Chenyu and CHENG Xiaoyue. Estimation of aircraft fuel consumption by modeling flight data from avionics systems[J]. Journal of Air Transport Management, 2022, 99: 102181. doi: 10.1016/j.jairtraman.2022.102181.
    [42]
    ZARIKOFF B, MARTIN D, and INSLEY M. Lightweight, low-cost and flexible flight data monitoring[C]. 2014 IEEE AUTOTEST, St. Louis, USA, 2014: 251–259. doi: 10.1109/AUTEST.2014.6935154.
    [43]
    TANGTHONG N and AKTIMAGOOL S. Management of laboratory-based learning activity on electronic flight instrument system[C]. 2021 6th International STEM Education Conference (iSTEM-Ed), Pattaya, Thailand, 2021: 1–4. doi: 10.1109/iSTEM-Ed52129.2021.9625098.
    [44]
    KHAN G A I and MAJID I. Modeling and simulation of interference between aircraft radar altimeter and 5G-C band transmissions along with implementation of proposed solution[C]. The International Conference on Aeronautical Sciences, Engineering and Technology, Muscat, Oman, 2024: 176–185. doi: 10.1007/978-981-99-7775-8_18.
    [45]
    CARROLL M, REBENSKY S, WILT D, et al. Integrating uncertified information from the electronic flight bag into the aircraft panel: Impacts on pilot response[J]. International Journal of Human-Computer Interaction, 2021, 37(7): 630–641. doi: 10.1080/10447318.2020.1854001.
    [46]
    TURTIAINEN H, COSTIN A, KHANDKER S, et al. GDL90fuzz: Fuzzing-GDL-90 data interface specification within aviation software and avionics devices–a cybersecurity pentesting perspective[J]. IEEE Access, 2022, 10: 21554–21562. doi: 10.1109/ACCESS.2022.3150840.
    [47]
    MANIKATH E, LI W C, and PIOTROWSKI P. Usability assessment on existing alerting designs for emergency communication between passengers and cabin crews[C]. The German Aerospace Conference (Deutscher Luft- und Raumfahrtkongress) 2023, Stuttgart, Germany, 2024: 610019. doi: 10.25967/610019.
    [48]
    METWALLY E A and MOHAMMED H T. Hacking an aircraft: Hacking the in-flight entertainment system[J]. Advances in Networks, 2022, 10(1): 7–14. doi: 10.11648/j.net.20221001.12.
    [49]
    AYUB S, PETRUNIN I, TSOURDOS A, et al. In-flight entertainment datalink analysis and simulation[C]. 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), San Antonio, USA, 2020: 1–10. doi: 10.1109/DASC50938.2020.9256432.
    [50]
    苟江, 梁卫星, 田伟, 等. 民机后舱一体化网络架构QoS算法设计[J]. 计算机与网络, 2024, 50(2): 156–162. doi: 10.20149/j.cnki.issn1008-1739.2024.02.011.

    GOU Jiang, LIANG Weixing, TIAN Wei, et al. Design of QoS algorithm for civil aircraft rear cabin integrated network architecture[J]. Computer & Network, 2024, 50(2): 156–162. doi: 10.20149/j.cnki.issn1008-1739.2024.02.011.
    [51]
    ZHOU Xuan, HE Feng, ZHAO Luxi, et al. Hybrid scheduling of tasks and messages for TSN-based avionics systems[J]. IEEE Transactions on Industrial Informatics, 2024, 20(2): 1081–1092. doi: 10.1109/TII.2023.3254608.
    [52]
    赵长啸, 汪克念, 张伟, 等. 民机航电系统功能-信息安全一体化分析方法[J]. 中国安全科学学报, 2022, 32(9): 49–56. doi: 10.16265/j.cnki.issn1003-3033.2022.09.2126.

    ZHAO Changxiao, WANG Kenian, ZHANG Wei, et al. Integrated analysis method of functional safety and cyber security of avionics system for civil aircraft[J]. China Safety Science Journal, 2022, 32(9): 49–56. doi: 10.16265/j.cnki.issn1003-3033.2022.09.2126.
    [53]
    SIDDIQUI F, AHLBRECHT A, KHAN R, et al. Cybersecurity engineering: Bridging the security gaps in avionics architectures and DO-326A/ED-202A[C]. 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), Barcelona, Spain, 2023: 1–8. doi: 10.1109/DASC58513.2023.10311187.
    [54]
    RAMANATT P R, NATARAJAN K, and SHOBHA K R. Challenges in implementing a wireless avionics network[J]. Aircraft Engineering and Aerospace Technology, 2020, 92(3): 482–494. doi: 10.1108/AEAT-07-2019-0144.
    [55]
    KULESHOV Y A, NAGPAL K, UCPINAR K, et al. Cyber attacks on avionics networks in digital twin environment: Detection and defense[C]. The AIAA SCITECH 2024 Forum, Orlando, USA, 2024: 0277. doi: 10.2514/6.2024-0277.
    [56]
    WU Zhijun, ZHANG Yun, YANG Yiming, et al. Spoofing and anti-spoofing technologies of global navigation satellite system: A survey[J]. IEEE Access, 2020, 8: 165444–165496. doi: 10.1109/ACCESS.2020.3022294.
    [57]
    VILLAGE A. DEF CON 28 aerospace village: Attacking flight management systems[J]. Retrieved Feb, 2020, 12: 2022.
    [58]
    SHATILIN I. Hacking an aircraft: Is it already real?[R]. 2015.
    [59]
    ISHTIAQ S and ABD RAHMAN N A. Cybersecurity vulnerabilities and defence techniques in aviation industry[C/OL]. The 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021), 2021: 559–567.
    [60]
    DORIGATTI D, STROHMEIER M, and NEUHAUS S. Air-bus hijacking: Silently taking over avionics systems[C]. The 10th ACM Cyber-Physical System Security Workshop, Singapore, 2024: 53–63. doi: 10.1145/3626205.3659144.
    [61]
    AL-SHAER R, SPRING J M, and CHRISTOU E. Learning the associations of MITRE ATT & CK adversarial techniques[C]. 2020 IEEE Conference on Communications and Network Security (CNS), Avignon, France, 2020: 1–9. doi: 10.1109/CNS48642.2020.9162207.
    [62]
    HILLEBRECHT A, MARKS T, and GOLLNICK V. An aeronautical data communication demand model for the North Atlantic oceanic airspace[J]. CEAS Aeronautical Journal, 2023, 14(2): 553–567. doi: 10.1007/s13272-023-00651-4.
    [63]
    BARON C and LOUIS V. Towards a continuous certification of safety-critical avionics software[J]. Computers in Industry, 2021, 125: 103382. doi: 10.1016/j.compind.2020.103382.
    [64]
    KAMBOJ P, KHARE S, and PAL S. User authentication using Blockchain based smart contract in role-based access control[J]. Peer-to-Peer Networking and Applications, 2021, 14(5): 2961–2976. doi: 10.1007/s12083-021-01150-1.
    [65]
    KO Y, KIM J, DUGUMA D G, et al. Drone secure communication protocol for future sensitive applications in military zone[J]. Sensors, 2021, 21(6): 2057. doi: 10.3390/s21062057.
    [66]
    JANGJOU M and SOHRABI M K. A comprehensive survey on security challenges in different network layers in cloud computing[J]. Archives of Computational Methods in Engineering, 2022, 29(6): 3587–3608. doi: 10.1007/s11831-022-09708-9.
    [67]
    ASANTE M, EPIPHANIOU G, MAPLE C, et al. Distributed ledger technologies in supply chain security management: A comprehensive survey[J]. IEEE Transactions on Engineering Management, 2023, 70(2): 713–739. doi: 10.1109/TEM.2021.3053655.
    [68]
    CASADO E, RODRIGUEZ R M, TABOSO P, et al. Information security in future air traffic management systems[J]. Journal of Aerospace Information Systems, 2016, 13(3): 101–112. doi: 10.2514/1.I010233.
    [69]
    PRANDINI M, PIRODDI L, PUECHMOREL S, et al. Toward air traffic complexity assessment in new generation air traffic management systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(3): 809–818. doi: 10.1109/TITS.2011.2113175.
    [70]
    STROHMEIER M, SCHAFER M, PINHEIRO R, et al. On perception and reality in wireless air traffic communication security[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(6): 1338–1357. doi: 10.1109/TITS.2016.2612584.
    [71]
    杨乐. 民航空管信息系统网络安全态势感知与分析[J]. 管理科学与工程, 2022, 11(4): 665–671. doi: 10.12677/MSE.2022.114079.

    YANG Le. Network security situation awareness and analysis of civil aviation management information system[J]. Management Science and Engineering, 2022, 11(4): 665–671. doi: 10.12677/MSE.2022.114079.
    [72]
    CHEN Shichuan, ZHENG Shilian, YANG Lifeng, et al. Deep learning for large-scale real-world ACARS and ADS-B radio signal classification[J]. IEEE Access, 2019, 7: 89256–89264. doi: 10.1109/ACCESS.2019.2925569.
    [73]
    GULTEPE G, KANAR T, ZIHIR S, et al. A 1024-element Ku-band SATCOM dual-polarized receiver with >10-dB/K G/T and embedded transmit rejection filter[J]. IEEE Transactions on Microwave Theory and Techniques, 2021, 69(7): 3484–3495. doi: 10.1109/TMTT.2021.3073321.
    [74]
    JIANG Wei, LIU Dan, CAI Baigen, et al. A fault-tolerant tightly coupled GNSS/INS/OVS integration vehicle navigation system based on an FDP algorithm[J]. IEEE Transactions on Vehicular Technology, 2019, 68(7): 6365–6378. doi: 10.1109/TVT.2019.2916852.
    [75]
    李腾耀, 王布宏, 尚福特, 等. ADS-B 攻击数据弹性恢复方法[J]. 电子与信息学报, 2020, 42(10): 2365–2373. doi: 10.11999/JEIT191020.

    LI Tengyao, WANG Buhong, SHANG Fute, et al. A resilient recovery method on ADS-B attack data[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2365–2373. doi: 10.11999/JEIT191020.
    [76]
    罗鹏, 王布宏, 李腾耀. 基于BiGRU-SVDD的ADS-B异常数据检测模型[J]. 航空学报, 2020, 41(10): 323878. doi: 10.7527/S1000-6893.2020.23878.

    LUO Peng, WANG Buhong, and LI Tengyao. ADS-B anomaly data detection model based on BiGRU-SVDD[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(10): 323878. doi: 10.7527/S1000-6893.2020.23878.
    [77]
    董襄宁, 赵征, 张洪海. 空中交通管理基础[M]. 北京: 科学出版社, 2011: 1–3.

    DONG Xiangning, ZHAO Zheng, and ZHANG Honghai. Fundamentals of Air Traffic Management[M]. Beijing: Science Press, 2011: 1–3.
    [78]
    WILLIAMSON T and SPENCER N A. Development and operation of the Traffic Alert and Collision Avoidance System (TCAS)[J]. Proceedings of the IEEE, 1989, 77(11): 1735–1744. doi: 10.1109/5.47735.
    [79]
    王布宏, 罗鹏, 李腾耀, 等. 基于粒子群优化多核支持向量数据描述的广播式自动相关监视异常数据检测模型[J]. 电子与信息学报, 2020, 42(11): 2727–2734. doi: 10.11999/JEIT190767.

    WANG Buhong, LUO Peng, LI Tengyao, et al. ADS-B anomalous data detection model based on PSO-MKSVDD[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2727–2734. doi: 10.11999/JEIT190767.
    [80]
    SHANG Fute, WANG Buhong, YAN Fuhu, et al. Multidevice false data injection attack models of ADS-B multilateration systems[J]. Security and Communication Networks, 2019, 2019(1): 8936784. doi: 10.1155/2019/8936784.
    [81]
    LI Tengyao, WANG Buhong, SHANG Fute, et al. Threat model and construction strategy on ADS-B attack data[J]. IET Information Security, 2020, 14(5): 542–552. doi: 10.1049/iet-ifs.2018.5635.
    [82]
    SHANG Fute, WANG Buhong, LI Tengyao, et al. Adversarial examples on deep-learning based ADS-B spoofing detection[J]. IEEE Wireless Communications Letters, 2020, 9(10): 1734–1737. doi: 10.1109/LWC.2020.3002914.
    [83]
    LI Tengyao, WANG Buhong, SHANG Fute, et al. Dynamic temporal ADS-B data attack detection based on sHDP-HMM[J]. Computers & Security, 2020, 93: 101789. doi: 10.1016/j.cose.2020.101789.
    [84]
    LI Tengyao, WANG Buhong, SHANG Fute, et al. Online sequential attack detection for ADS-B data based on hierarchical temporal memory[J]. Computers & Security, 2019, 87: 101599. doi: 10.1016/j.cose.2019.101599.
    [85]
    LI Tengyao and WANG Buhong. Sequential collaborative detection strategy on ADS-B data attack[J]. International Journal of Critical Infrastructure Protection, 2019, 24: 78–99. doi: 10.1016/j.ijcip.2018.11.003.
    [86]
    ESKILSSON S, GUSTAFSSON H, KHAN S, et al. Demonstrating ADS-B AND CPDLC attacks with software-defined radio[C]. 2020 Integrated Communications Navigation and Surveillance Conference (ICNS), Herndon, USA, 2020: 1B2-1–1B2-9. doi: 10.1109/ICNS50378.2020.9222945.
    [87]
    NGUYEN L K, NGUYEN D H N, TRAN N H, et al. SATCOM jamming resiliency under non-uniform probability of attacks[C]. The MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM), San Diego, USA, 2021: 85–90. doi: 10.1109/MILCOM52596.2021.9652944.
    [88]
    LI Xinwei, ZHANG Qianyun, XU Lexi, et al. A compatible and identity privacy-preserving security protocol for ACARS[C]. 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Wuhan, China, 2022: 1048–1053. doi: 10.1109/TrustCom56396.2022.00143.
    [89]
    CHOUDHARY G, SIHAG V, GUPTA S, et al. Aviation attacks based on ILS and VOR vulnerabilities[J]. Journal of Surveillance, Security and Safety, 2022, 3: 27–40. doi: 10.20517/jsss.2021.17.
    [90]
    YANG Zhen, YING Jun, SHEN Junjie, et al. Anomaly detection against GPS spoofing attacks on connected and autonomous vehicles using learning from demonstration[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(9): 9462–9475. doi: 10.1109/TITS.2023.3269029.
    [91]
    OBOD I, SVYD I, ZAVOLODKO G, et al. Assessing SSR relative data capacity[C]. 2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineering (UKRCON), Lviv, Ukraine, 2021: 142–146. doi: 10.1109/UKRCON53503.2021.9575971.
    [92]
    ZHANG Qianyun, WANG Zhendong, WU Biyi, et al. A Robust and Practical Solution to ADS-B security against denial-of-service attacks[J]. IEEE Internet of Things Journal, 2024, 11(8): 13647–13659. doi: 10.1109/JIOT.2023.3337543.
    [93]
    MONTEIRO M. Detecting malicious ADS-B broadcasts using wide area multilateration[C]. 2015 IEEE/AIAA 34th Digital Avionics Systems Conference (DASC), Prague, Czech Republic, 2015: 1–28. doi: 10.1109/DASC.2015.7311579.
    [94]
    LUO Peng, WANG Buhong, LI Tengyao, et al. ADS-B anomaly data detection model based on VAE-SVDD[J]. Computers & Security, 2021, 104: 102213. doi: 10.1016/j.cose.2021.102213.
    [95]
    LUO Peng, WANG Buhong, and TIAN Jiwei. TTSAD: TCN-Transformer-SVDD Model for Anomaly Detection in air traffic ADS-B data[J]. Computers & Security, 2024, 141: 103840. doi: 10.1016/j.cose.2024.103840.
    [96]
    LUO Peng, WANG Buhong, TIAN Jiwei, et al. ADS-Bpois: Poisoning attacks against deep-learning-based air traffic ADS-B unsupervised anomaly detection models[J]. IEEE Internet of Things Journal, 2024, 11(23): 38301–38311. doi: 10.1109/JIOT.2024.3446675.
    [97]
    LUO Peng, WANG Buhong, TIAN Jiwei, et al. Adversarial attacks against deep-learning-based automatic dependent surveillance-broadcast unsupervised anomaly detection models in the context of air traffic management[J]. Sensors, 2024, 24(11): 3584. doi: 10.3390/s24113584.
    [98]
    MCPARLAND T, PATEL V, and HUGHES W J. Securing air-ground communications[C]. The 20th Digital Avionics Systems Conference, Daytona Beach, USA, 2001: 7A7/1–7A7/9. doi: 10.1109/DASC.2001.964187.
    [99]
    SCHRAML M G and KNOPP A. Physical layer security with unknown eavesdroppers in beyond-5G MU-MIMO SATCOM[C]. Proceedings of 2020 IEEE 3rd 5G World Forum (5GWF), Bangalore, India, 2020: 180–185. doi: 10.1109/5GWF49715.2020.9221107.
    [100]
    YUE Meng and WU Xiaofeng. The approach of ACARS data encryption and authentication[C]. 2010 International Conference on Computational Intelligence and Security, Nanning, China, 2010: 556–560. doi: 10.1109/CIS.2010.127.
    [101]
    ADAMY D. EW 102: A Second Course in Electronic Warfare[M]. Norwood, USA: Artech House, 2003.
    [102]
    BLANCH J, WALTER T, and ENGE P. Satellite navigation for aviation in 2025[J]. Proceedings of the IEEE, 2012, 100(Special Centennial Issue): 1821–1830. doi: 10.1109/JPROC.2012.2190154.
    [103]
    STROHMEIER M, LENDERS V, and MARTINOVIC I. Intrusion detection for airborne communication using PHY-layer information[C]. The 12th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, Milan, Italy, 2015: 67–77. doi: 10.1007/978-3-319-20550-2_4.
    [104]
    CHIANG J T, HAAS J J, CHOI J, et al. Secure location verification using simultaneous multilateration[J]. IEEE Transactions on Wireless Communications, 2012, 11(2): 584–591. doi: 10.1109/TWC.2011.120911.101147.
    [105]
    MUNIR A, AVED A, and BLASCH E. Situational awareness: Techniques, challenges, and prospects[J]. AI, 2022, 3(1): 55–77. doi: 10.3390/ai3010005.
    [106]
    WU Zhijun, XU Pei, and FAN Haoyu. Network security situation assessment method based eigenvector centrality[C]. 2024 International Wireless Communications and Mobile Computing (IWCMC), Ayia Napa, Cyprus, 2024: 103–108. doi: 10.1109/IWCMC61514.2024.10592357.
    [107]
    WU Zhijun and FAN Haoyu. Aviation network security situation awareness based on game theory[C]. 2023 IEEE Aerospace Conference, Big Sky, USA, 2023: 1–8. doi: 10.1109/AERO55745.2023.10115582.
    [108]
    WU Zhijun, BAI Zhuoning, ZHANG Lizhe, et al. Feature extraction method based on sparse autoencoder for air traffic management system security situation awareness[J]. Security and Communication Networks, 2022, 2022(1): 3757662. doi: 10.1155/2022/3757662.
    [109]
    MA Lan, MA Shaopu, and WU Zhijun. WNN-based prediction of security situation awareness for the civil aviation network[J]. Journal of Intelligent Systems, 2014, 24(1): 55–67. doi: 10.1515/jisys-2014-0004.
    [110]
    SMITH M, MOSER D, STROHMEIER M, et al. Undermining privacy in the Aircraft Communications Addressing and Reporting System (ACARS)[J]. Proceedings on Privacy Enhancing Technologies, 2018, 2018(3): 105–122. doi: 10.1515/popets-2018-0023.
    [111]
    UKWANDU E, BEN-FARAH M, HINDY H, et al. Cyber-security challenges in aviation industry: A review of current and future trends[J]. Information, 2022, 13(3): 146. doi: 10.3390/info13030146.
    [112]
    吴志军. 广域信息管理SWIM信息安全关键技术[M]. 北京: 人民邮电出版社, 2020: 13–19.

    WU Zhijun. Key Technologies of Information Assurance for System Wide Information Management (SWIM)[M]. Beijing: Posts & Telecommunications Press, 2020: 13–19.
    [113]
    李晛. 基于物联网技术的智慧机场设计与应用探讨[J]. 工程管理, 2024, 5(7): 14–15. doi: 10.12238/jpm.v5i7.6950.

    LI Xian. Discussion on the smart airport design and application based on the Internet of Things technology[J]. Journal of Project Management, 2024, 5(7): 14–15. doi: 10.12238/jpm.v5i7.6950.
    [114]
    顾兆军, 张一诺, 宋跃东, 等. 智慧机场物联网应用及网络安全挑战[J]. 指挥信息系统与技术, 2023, 14(5): 14–20. doi: 10.15908/j.cnki.cist.2023.05.003.

    GU Zhaojun, ZHANG Yinuo, SONG Yuedong, et al. IoT applications and cyber security challenges in smart airports[J]. Command Information System and Technology, 2023, 14(5): 14–20. doi: 10.15908/j.cnki.cist.2023.05.003.
    [115]
    MAJID S A, NUGRAHA A, SULISTIYONO B S, et al. The effect of safety risk management and airport personnel competency on aviation safety performance[J]. Uncertain Supply Chain Management, 2022, 10(4): 1509–1522. doi: 10.5267/j.uscm.2022.6.004.
    [116]
    XIONG Wenjun and LAGERSTRÖM R. Threat modeling–a systematic literature review[J]. Computers & Security, 2019, 84: 53–69. doi: 10.1016/j.cose.2019.03.010.
    [117]
    邢馨心, 左青雅, 刘建伟. 基于5G的智慧机场网络安全方案设计与安全性分析[J]. 网络与信息安全学报, 2023, 9(5): 116–126. doi: 10.11959/j.issn.2096-109x.2023075.

    XING Xinxin, ZUO Qingya, and LIU Jianwei. 5G-based smart airport network security scheme design and security analysis[J]. Chinese Journal of Network and Information Security, 2023, 9(5): 116–126. doi: 10.11959/j.issn.2096-109x.2023075.
    [118]
    FLORIDO-BENÍTEZ L. Identifying and classifying cyberattacks on airports[J]. Cyber Security: A Peer-Reviewed Journal, 2024, 8(1): 63–79. doi: 10.69554/TWHW5595.
    [119]
    SUCIU G, SCHEIANU A, VULPE A, et al. Cyber-attacks-the impact over airports security and prevention modalities[C]. The WorldCIST: World Conference on Information Systems and Technologies, Naples, Italy, 2018: 154–162. doi: 10.1007/978-3-319-77700-9_16.
    [120]
    KALAIVANI N, RAMAN R, BASAVARADDI C C S, et al. Enhancing air travel with IoT: Smart airports and passenger experience[C]. 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2023: 1300–1305. doi: 10.1109/ICECA58529.2023.10394746.
    [121]
    SAADA H, ORIZIO R, and SEBASTIO S. Modeling and conducting security risk assessment of smart airport infrastructures with SecRAM[C]. The 7th International Conference on Networking, Intelligent Systems and Security, Meknes, Morocco, 2024: 59. doi: 10.1145/3659677.3659992.
    [122]
    ALLABERGANOV B A and ABDULLAYEV D. Models for protecting airport information systems from cyber incidents[J]. Science and Innovation, 2022, 1(8): 919–923. doi: 10.5281/zenodo.7445057.
    [123]
    FERON E M, SANNI O, MOTE M, et al. Ariadne: A common-sense thread for enabling provable safety in air mobility systems with unreliable components[C]. The AIAA SciTech 2022 Forum, San Diego, USA, 2022: 0057. doi: 10.2514/6.2022-0057.
    [124]
    ABOAOJA F A, ZAINAL A, GHALEB F A, et al. Malware detection issues, challenges, and future directions: A survey[J]. Applied Sciences, 2022, 12(17): 8482. doi: 10.3390/app12178482.
    [125]
    ABELSON H, ANDERSON R, BELLOVIN S M, et al. Bugs in our pockets: The risks of client-side scanning[J]. Journal of Cybersecurity, 2024, 10(1): tyad020. doi: 10.1093/cybsec/tyad020.
    [126]
    LI Tong, WANG Xiaowei, and NI Yeming. Aligning social concerns with information system security: A fundamental ontology for social engineering[J]. Information Systems, 2022, 104: 101699. doi: 10.1016/j.is.2020.101699.
    [127]
    ELSALLAMY S and ABD RAHMAN N A. AI implementation in airport system: A study of vulnerabilities and countermeasures[J]. Journal of Applied Technology and Innovation, 2022, 6(2): 11–16.
    [128]
    SEN S, AYDOGAN E, and AYSAN A I. Coevolution of mobile malware and anti-malware[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(10): 2563–2574. doi: 10.1109/TIFS.2018.2824250.
    [129]
    丁磊, 韩建云, 张西武, 等. 智慧机场物联网系统安全防护研究[J]. 民航学报, 2021, 5(5): 81–84. doi: 10.3969/j.issn.2096-4994.2021.05.021.

    DING Lei, HAN Jianyun, ZHANG Xiwu, et al. Research on cyber security protection of IOT system of smart airport[J]. Journal of Civil Aviation, 2021, 5(5): 81–84. doi: 10.3969/j.issn.2096-4994.2021.05.021.
    [130]
    SCOZZARO G, MUJICA MOTA M, DELAHAYE D, et al. Simulation-optimisation-based decision support system for managing airport security resources[C]. The 11th Congress on Simulation for a Sustainable Future, Amsterdam, The Netherlands, 2023: 140–155. doi: 10.1007/978-3-031-68438-8_11.
    [131]
    ALSULAMI A A and ZEIN-SABATTO S. Resilient cyber-security approach for aviation cyber-physical systems protection against sensor spoofing attacks[C]. Proceedings of 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), USA, 2021: 565–571. doi: 10.1109/CCWC51732.2021.9376158.
    [132]
    ABDELGHANI T. Implementation of defense in depth strategy to secure industrial control system in critical infrastructures[J]. American Journal of Artificial Intelligence, 2019, 3(2): 17–22. doi: 10.11648/j.ajai.20190302.11.
    [133]
    VYAS S, HANNAY J, BOLTON A, et al. Automated cyber defence: A review[EB/OL]. https://arxiv.org/abs/2303.04926, 2023.
    [134]
    ALABBAD M, MHASKAR N, and KHEDRI R. Hardening of network segmentation using automated referential penetration testing[J]. Journal of Network and Computer Applications, 2024, 224: 103851. doi: 10.1016/j.jnca.2024.103851.
    [135]
    ASHRAFI R and ALKINDI H. A framework for IS/IT disaster recovery planning[J]. International Journal of Business Continuity and Risk Management, 2022, 12(1): 1–21. doi: 10.1504/IJBCRM.2022.121645.
    [136]
    BEAN B. Mitigating insider threats in the domestic aviation system: Policy options for TSA[J]. Homeland Security Affairs, 2017.
    [137]
    CORNACCHIA M, PAPA F, and SAPIO B. User acceptance of voice biometrics in managing the physical access to a secure area of an international airport[J]. Technology Analysis & Strategic Management, 2020, 32(10): 1236–1250. doi: 10.1080/09537325.2020.1758655.
    [138]
    CURRAH P and MULQUEEN T. Securitizing gender: Identity, biometrics, and transgender bodies at the airport[J]. Social Research: An International Quarterly, 2011, 78(2): 557–582. doi: 10.1353/sor.2011.0030.
    [139]
    KARAMITSOS I, PAPADAKI M, AL-HUSSAENI K, et al. Transforming airport security: Enhancing efficiency through blockchain smart contracts[J]. Electronics, 2023, 12(21): 4492. doi: 10.3390/electronics12214492.
    [140]
    MALIK H, TAHIR S, TAHIR H, et al. A homomorphic approach for security and privacy preservation of Smart Airports[J]. Future Generation Computer Systems, 2023, 141: 500–513. doi: 10.1016/j.future.2022.12.005.
    [141]
    ALI A S and HASAN D S. An IoT-based smart airport check-in system via three-factor authentication (3FA)[J]. Zanco Journal of Pure and Applied Sciences, 2023, 35(4): 1–13. doi: 10.21271/ZJPAS.35.4.01.
    [142]
    NIRAULA M. Cybersecurity and interoperability of aviation safety service ecosystem[C]. 2022 Integrated Communication, Navigation and Surveillance Conference (ICNS), Dulles, USA, 2022: 1–12. doi: 10.1109/ICNS54818.2022.9771482.
    [143]
    SUCIU G, SCHEIANU A, PETRE I, et al. Cybersecurity threats analysis for airports[C]. Proceedings of the World Conference on Information Systems and Technologies, Galicia, Spain, 2019: 252–262. doi: 10.1007/978-3-030-16184-2_25.
    [144]
    ZORLU O, OZSOY A, SERT S A. A role-based access control management model on blockchain for restricted facilities: An airport example[C]. 2023 10th International Conference on Recent Advances in Air and Space Technologies (RAST), Istanbul, Turkiye, 2023: 1–6. doi: 10.1109/RAST57548.2023.10197974.
    [145]
    WILLEMSEN B and CADEE M. Extending the airport boundary: Connecting physical security and cybersecurity[J]. Journal of Airport Management, 2018, 12(3): 236–247. doi: 10.69554/UVLU6436.
    [146]
    MENZEL D and HESTERMAN J. Airport security threats and strategic options for mitigation[J]. Journal of Airport Management, 2018, 12(2): 118–131. doi: 10.69554/EFBM2046.
    [147]
    ZHOU Zhipeng, YU Xinhui, ZHU Zeyu, et al. Development and application of a Bayesian network-based model for systematically reducing safety risks in the commercial air transportation system[J]. Safety Science, 2023, 157: 105942. doi: 10.1016/j.ssci.2022.105942.
    [148]
    EILSTRUP-SANGIOVANNI M. Ordering global governance complexes: The evolution of the governance complex for international civil aviation[J]. The Review of International Organizations, 2022, 17(2): 293–322. doi: 10.1007/s11558-020-09411-z.
    [149]
    Federal Aviation Administration. NAS operational view[EB/OL]. https://www.faa.gov/about/office_org/headquarters_offices/ang/offices/tc/library/Storyboard/nextgen-overview.html, 2022.
    [150]
    SUN Mengyuan, TIAN Yong, WANG Xunuo, et al. Transport causality knowledge-guided GCN for propagated delay prediction in airport delay propagation networks[J]. Expert Systems with Applications, 2024, 240: 122426. doi: 10.1016/j.eswa.2023.122426.
    [151]
    CAI Kaiquan, LI Yue, ZHU Yongwen, et al. A geographical and operational deep graph convolutional approach for flight delay prediction[J]. Chinese Journal of Aeronautics, 2023, 36(3): 357–367. doi: 10.1016/j.cja.2022.10.004.
    [152]
    CAI Kaiquan, LI Yue, FANG Yiping, et al. A deep learning approach for flight delay prediction through time-evolving graphs[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 11397–11407. doi: 10.1109/TITS.2021.3103502.
    [153]
    GUO Ziyu, MEI Guangxu, LIU Shijun, et al. SGDAN—A spatio-temporal graph dual-attention neural network for quantified flight delay prediction[J]. Sensors, 2020, 20(22): 6433. doi: 10.3390/s20226433.
    [154]
    LI Chi, QI Xixian, YANG Yuzhe, et al. FAST-CA: Fusion-based adaptive spatial-temporal learning with coupled attention for airport network delay propagation prediction[J]. Information Fusion, 2024, 107: 102326. doi: 10.1016/j.inffus.2024.102326.
    [155]
    RUAN J H, WANG Z X, CHAN F T S, et al. A reinforcement learning-based algorithm for the aircraft maintenance routing problem[J]. Expert Systems with Applications, 2021, 169: 114399. doi: 10.1016/j.eswa.2020.114399.
    [156]
    OSZUST M, KAPUSCINSKI T, WARCHOL D, et al. A vision-based method for supporting autonomous aircraft landing[J]. Aircraft Engineering and Aerospace Technology, 2018, 90(6): 973–982. doi: 10.1108/AEAT-11-2017-0250.
    [157]
    GAO Ruizhen, CHEN Meng, ZHAO Ziyue, et al. Real-time detection algorithm of aircraft landing gear based on improved YOLOv8[J]. 2024. doi: 10.21203/rs.3.rs-4493909/v1.
    [158]
    AMIT R A and MOHAN C K. A robust airport runway detection network based on R-CNN using remote sensing images[J]. IEEE Aerospace and Electronic Systems Magazine, 2021, 36(11): 4–20. doi: 10.1109/MAES.2021.3088477.
    [159]
    LIU Mingkun, FENG Guangkun, XU Tingbing, et al. Fusing dense features and pose consistency: A regression method for attitude measurement of aircraft landing[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 5007913. doi: 10.1109/TIM.2023.3244803.
    [160]
    SHI Qingbang and LI Jun. Objects detection of UAV for anti-UAV based on YOLOv4[C]. 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology, Weihai, China, 2020: 1048–1052. doi: 10.1109/ICCASIT50869.2020.9368788.
    [161]
    ZHAO Jie, ZHANG Jingshu, LI Dongdong, et al. Vision-based anti-UAV detection and tracking[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 25323–25334. doi: 10.1109/TITS.2022.3177627.
    [162]
    WANG Chuanyu, WANG Tian, WANG Ershen, et al. Flying small target detection for anti-UAV based on a Gaussian mixture model in a compressive sensing domain[J]. Sensors, 2019, 19(9): 2168. doi: 10.3390/s19092168.
    [163]
    KANAVOS A, KOUNELIS F, ILIADIS L, et al. Deep learning models for forecasting aviation demand time series[J]. Neural Computing and Applications, 2021, 33(23): 16329–16343. doi: 10.1007/s00521-021-06232-y.
    [164]
    GU Weifan, GUO Baohua, ZHANG Zhezhe, et al. Civil aviation passenger traffic forecasting: Application and comparative study of the seasonal autoregressive integrated moving average model and backpropagation neural network[J]. Sustainability, 2024, 16(10): 4110. doi: 10.3390/su16104110.
    [165]
    BRITTAIN M and WEI Peng. Scalable autonomous separation assurance with heterogeneous multi-agent reinforcement learning[J]. IEEE Transactions on Automation Science and Engineering, 2022, 19(4): 2837–2848. doi: 10.1109/TASE.2022.315160.
    [166]
    KRAVARIS T, LENTZOS K, SANTIPANTAKIS G, et al. Explaining deep reinforcement learning decisions in complex multiagent settings: Towards enabling automation in air traffic flow management[J]. Applied Intelligence, 2023, 53(4): 4063–4098. doi: 10.1007/s10489-022-03605-1.
    [167]
    WANG Yuan, CAI Weilin, TU Yilei, et al. Reinforcement-learning-informed prescriptive analytics for air traffic flow management[J]. IEEE Transactions on Automation Science and Engineering, 2024, 21(3): 4188–4202. doi: 10.1109/TASE.2023.3292921.
    [168]
    SPATHARIS C, BASTAS A, KRAVARIS T, et al. Hierarchical multiagent reinforcement learning schemes for air traffic management[J]. Neural Computing and Applications, 2023, 35(1): 147–159. doi: 10.1007/S00521-021-05748-7.
    [169]
    PAPADOPOULOS G, BASTAS A, VOUROS G A, et al. Deep reinforcement learning in service of air traffic controllers to resolve tactical conflicts[J]. Expert Systems with Applications, 2024, 236: 121234. doi: 10.1016/j.eswa.2023.121234.
    [170]
    BAUMANN S and KLINGAUF U. Modeling of aircraft fuel consumption using machine learning algorithms[J]. CEAS Aeronautical Journal, 2020, 11(1): 277–287. doi: 10.1007/s13272-019-00422-0.
    [171]
    ZHU Xinting and LI Lishuai. Flight time prediction for fuel loading decisions with a deep learning approach[J]. Transportation Research Part C: Emerging Technologies, 2021, 128: 103179. doi: 10.1016/j.trc.2021.103179.
    [172]
    METLEK S. A new proposal for the prediction of an aircraft engine fuel consumption: A novel CNN-BiLSTM deep neural network model[J]. Aircraft Engineering and Aerospace Technology, 2023, 95(5): 838–848. doi: 10.1108/AEAT-05-2022-0132.
    [173]
    EUROCONTROL. AI/ML based augmented 4D trajectory[EB/OL]. https://www.eurocontrol.int/publication/aiml-based-augmented-4d-trajectory, 2021.
    [174]
    SHI Zhiyuan, XU Min, PAN Quan, et al. LSTM-based flight trajectory prediction[C]. 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018: 1–8. doi: 10.1109/IJCNN.2018.8489734.
    [175]
    ZHANG Xiaoge and MAHADEVAN S. Bayesian neural networks for flight trajectory prediction and safety assessment[J]. Decision Support Systems, 2020, 131: 113246. doi: 10.1016/j.dss.2020.113246.
    [176]
    ZHANG Zheng, GUO Dongyue, ZHOU Shizhong, et al. Flight trajectory prediction enabled by time-frequency wavelet transform[J]. Nature Communications, 2023, 14(1): 5258. doi: 10.1038/s41467-023-40903-9.
    [177]
    SHI Zhiyuan, XU Min, and PAN Quan. 4-D flight trajectory prediction with constrained LSTM network[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(11): 7242–7255. doi: 10.1109/TITS.2020.3004807.
    [178]
    CHEN C J, HUANG C N, and YANG S M. Application of deep learning to multivariate aviation weather forecasting by long short-term memory[J]. Journal of Intelligent & Fuzzy Systems, 2023, 44(3): 4987–4997. doi: 10.3233/JIFS-223183.
    [179]
    YUAN Ligang, ZENG Yang, CHEN Haiyan, et al. Terminal traffic situation prediction model under the influence of weather based on deep learning approaches[J]. Aerospace, 2022, 9(10): 580. doi: 10.3390/aerospace9100580.
    [180]
    CHEN C J, HUANG C N, and YANG S M. Aviation visibility forecasting by integrating Convolutional Neural Network and long short-term memory network[J]. Journal of Intelligent & Fuzzy Systems, 2023, 45(3): 5007–5020. doi: 10.3233/JIFS-230483.
    [181]
    ZHAO Tianyu, ESCRIBANO J, MAJUMDAR A, et al. Spatiotemporal thunderstorm forecasting for pre-tactical air traffic operation: A deep learning approach[C]. Proceedings of AIAA Aviation Forum and Ascend 2024, Las Vegas, Nevada, 2024: 4634. doi: 10.2514/6.2024-4634.
    [182]
    AYHAN B, VARGO E P, and TANG Huang. On the exploration temporal fusion transformers for anomaly detection with multivariate aviation time-series data[J]. Aerospace, 2024, 11(8): 646. doi: 10.3390/aerospace11080646.
    [183]
    ZHOU Wentao, CAI Chengtao, WU Kejun, et al. Las-yolo: A lightweight detection method based on YOLOv7 for small objects in airport surveillance[J]. The Journal of Supercomputing, 2024, 80(15): 21764–21789. doi: 10.1007/S11227-024-06289-1.
    [184]
    ZHANG Xiang, SHU Chang, LI Shuai, et al. AGVS: A new change detection dataset for airport ground video surveillance[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 20588–20600. doi: 10.1109/TITS.2022.3184978.
    [185]
    LI Weidong, LIU Jia, and MEI Hang. Lightweight convolutional neural network for aircraft small target real-time detection in Airport videos in complex scenes[J]. Scientific Reports, 2022, 12(1): 14474. doi: 10.1038/s41598-022-18263-z.
    [186]
    ZHOU Wentao, CAI Chengtao, ZHENG Liying, et al. ASSD-YOLO: A small object detection method based on improved YOLOv7 for airport surface surveillance[J]. Multimedia Tools and Applications, 2024, 83(18): 55527–55548. doi: 10.1007/S11042-023-17628-4.
    [187]
    ZHANG Mengmei, WANG Xiao, ZHU Meiqi, et al. Robust heterogeneous graph neural networks against adversarial attacks[C]. The 36th AAAI Conference on Artificial Intelligence, 2022, 36(4): 4363–4370. doi: 10.1609/aaai.v36i4.20357.
    [188]
    LIN Xixun, ZHOU Chuan, WU Jia, et al. Exploratory adversarial attacks on graph neural networks for semi-supervised node classification[J]. Pattern Recognition, 2023, 133: 109042. doi: 10.1016/j.patcog.2022.109042.
    [189]
    FINKELSHTEIN B, BASKIN C, ZHELTONOZHSKII E, et al. Single-node attacks for fooling graph neural networks[J]. Neurocomputing, 2022, 513: 1–12. doi: 10.1016/j.neucom.2022.09.115.
    [190]
    ZHANG Haotian and MA Xu. Misleading attention and classification: An adversarial attack to fool object detection models in the real world[J]. Computers & Security, 2022, 122: 102876. doi: 10.1016/j.cose.2022.102876.
    [191]
    LI Guopeng, XU Yue, DING Jian, et al. Towards generic and controllable attacks against object detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5635812. doi: 10.1109/TGRS.2024.3417958.
    [192]
    WU Tao, WANG Xuechun, QIAO Shaojie, et al. Small perturbations are enough: Adversarial attacks on time series prediction[J]. Information Sciences, 2022, 587: 794–812. doi: 10.1016/j.ins.2021.11.007.
    [193]
    KARIM F, MAJUMDAR S, and DARABI H. Adversarial attacks on time series[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(10): 3309–3320. doi: 10.1109/TPAMI.2020.2986319.
    [194]
    LIU Guanlin and LAI Lifeng. Efficient adversarial attacks on online multi-agent reinforcement learning[C]. The 37th International Conference on Neural Information Processing Systems, New Orleans, USA, 2023: 1062.
    [195]
    YOU Qiaoben, YING Chengyang, ZHOU Xinning, et al. Understanding adversarial attacks on observations in deep reinforcement learning[J]. Science China Information Sciences, 2024, 67(5): 152104. doi: 10.1007/s11432-021-3688-y.
    [196]
    SHEN Yun, HE Xinlei, HAN Yufei, et al. Model stealing attacks against inductive graph neural networks[C]. 2022 IEEE Symposium on Security and Privacy (SP), San Francisco, USA, 2022: 1175–1192. doi: 10.1109/SP46214.2022.9833607.
    [197]
    HE X, JIA J, BACKES M, et al. Stealing links from graph neural networks[C/OL]. Proceedings of the 30th USENIX Security Symposium (USENIX Security 21), 2021: 2669–2686.
    [198]
    PODHAJSKI M, DUBIŃSKI J, BOENISCH F, et al. Efficient model-stealing attacks against inductive graph neural networks[J]. Frontiers in Artificial Intelligence and Applications, 2024, 392: 1438–1445. doi: 10.3233/FAIA240646.
    [199]
    XU Jing, XUE Minhui, and PICEK S. Explainability-based backdoor attacks against graph neural networks[C]. The 3rd ACM Workshop on Wireless Security and Machine Learning, Abu Dhabi, United Arab Emirates, 2021: 31–36. doi: 10.1145/3468218.3469046.
    [200]
    ZHENG Haibin, XIONG Haiyang, CHEN Jinyin, et al. Motif-backdoor: Rethinking the backdoor attack on graph neural networks via motifs[J]. IEEE Transactions on Computational Social Systems, 2024, 11(2): 2479–2493. doi: 10.1109/TCSS.2023.3267094.
    [201]
    CHAN S H, DONG Yinpeng, ZHU Jun, et al. BadDet: Backdoor attacks on object detection[C]. The European Conference on Computer Vision, Tel Aviv, Israel, 2022: 396–412. doi: 10.1007/978-3-031-25056-9_26.
    [202]
    LUO Chengxiao, LI Yiming, JIANG Yong, et al. Untargeted backdoor attack against object detection[C]. Proceedings of 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023: 1–5. doi: 10.1109/ICASSP49357.2023.10095980.
    [203]
    KIOURTI P, WARDEGA K, JHA S, et al. TrojDRL: Evaluation of backdoor attacks on deep reinforcement learning[C]. 2020 57th ACM/IEEE Design Automation Conference (DAC), San Francisco, USA, 2020: 1–6. doi: 10.1109/DAC18072.2020.9218663.
    [204]
    CHEN Yanjiao, ZHENG Zhicong, and GONG Xueluan. MARNet: Backdoor attacks against cooperative multi-agent reinforcement learning[J]. IEEE Transactions on Dependable and Secure Computing, 2023, 20(5): 4188–4198. doi: 10.1109/TDSC.2022.3207429.
    [205]
    HASHEMI S M, BOTEZ R M, and GRIGORIE T L. New reliability studies of data-driven aircraft trajectory prediction[J]. Aerospace, 2020, 7(10): 145. doi: 10.3390/aerospace7100145.
    [206]
    DONG Guimin, TANG Mingyue, WANG Zhiyuan, et al. Graph neural networks in IoT: A survey[J]. ACM Transactions on Sensor Networks, 2023, 19(2): 47. doi: 10.1145/3565973.
    [207]
    KIM S and PARK K J. A survey on machine-learning based security design for cyber-physical systems[J]. Applied Sciences, 2021, 11(12): 5458. doi: 10.3390/app11125458.
    [208]
    VAN IERSEL Q G, MURRIETA MENDOZA A, FELIX PATRON R S, et al. Attack and defense on aircraft trajectory prediction algorithms[C]. The AIAA AVIATION 2022 Forum, Chicago, USA, 2022: 4027. doi: 10.2514/6.2022-4027.
    [209]
    KIANPOUR M and WEN Shaofang. Timing attacks on machine learning: State of the art[C]. 2019 Intelligent Systems Conference, London, UK, 2020: 111–125. doi: 10.1007/978-3-030-29516-5_10.
    [210]
    MADRY A, MAKELOV A, SCHMIDT L, et al. Towards deep learning models resistant to adversarial attacks[C]. Proceedings of the 6th International Conference on Learning Representations, Vancouver, Canada, 2017: 1–23.
    [211]
    MIYATO T, MAEDA S, KOYAMA M, et al. Virtual adversarial training: A regularization method for supervised and semi-supervised learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(8): 1979–1993. doi: 10.1109/TPAMI.2018.2858821.
    [212]
    YE Hongwei, LIU Xiaozhang, and LI Chunlai. DSCAE: A denoising sparse convolutional autoencoder defense against adversarial examples[J]. Journal of Ambient Intelligence and Humanized Computing, 2022, 13(3): 1419–1429. doi: 10.1007/s12652-020-02642-3.
    [213]
    XU Weilin, EVANS D, and QI Yanjun. Feature squeezing: Detecting adversarial examples in deep neural networks[EB/OL]. https://arxiv.org/abs/1704.01155, 2017.
    [214]
    TRAMÈR F, KURAKIN A, PAPERNOT N, et al. Ensemble adversarial training: Attacks and defenses[EB/OL]. https://arxiv.org/abs/1705.07204, 2017.
    [215]
    KUZLU M, CATAK F O, CALI U, et al. Adversarial security mitigations of mmWave beamforming prediction models using defensive distillation and adversarial retraining[J]. International Journal of Information Security, 2023, 22(2): 319–332. doi: 10.1007/s10207-022-00644-0.
    [216]
    TSENG F H, ZENG Jiangyi, CHO H H, et al. Detecting adversarial examples of fake news via the neurons activation state[J]. IEEE Transactions on Computational Social Systems, 2024, 11(4): 5199–5209. doi: 10.1109/TCSS.2023.3293718.
    [217]
    ZHANG Lei, ZHOU Yuhang, YANG Yi, et al. Meta invariance defense towards generalizable robustness to unknown adversarial attacks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(10): 6669–6687. doi: 10.1109/TPAMI.2024.3385745.
    [218]
    KHALED K, DHAOUADI M, DE MAGALHAES F G, et al. Efficient defense against model stealing attacks on convolutional neural networks[C]. 2023 International Conference on Machine Learning and Applications (ICMLA), Jacksonville, USA, 2023: 45–52. DOI: 10.1109/ICMLA58977.2023.00015.
    [219]
    郭鑫. 基于决策空间的模型窃取攻击检测与防御方法[D]. [硕士论文], 西安电子科技大学, 2023.

    GUO Xin. Decision space based approach to detect and defend against model stealing attacks[D]. [Master dissertation], Xidian University, 2023.
    [220]
    LI Yiming, ZHU Linghui, JIA Xiaojun, et al. Defending against model stealing via verifying embedded external features[C]. The 36th AAAI Conference on Artificial Intelligence, Palo Alto, USA, 2022: 1464–1472. doi: 10.1609/aaai.v36i2.20036.
    [221]
    THUDUMU S, BRANCH P, JIN Jiong, et al. A comprehensive survey of anomaly detection techniques for high dimensional big data[J]. Journal of Big Data, 2020, 7: 42. doi: 10.1186/s40537-020-00320-x.
    [222]
    GU Tianyu, DOLAN-GAVITT B, and GARG S. BadNets: Identifying vulnerabilities in the machine learning model supply chain[EB/OL]. https://arxiv.org/abs/1708.06733, 2017.
    [223]
    WAN Li, ZEILER M, ZHANG Sixin, et al. Regularization of neural networks using dropconnect[C]. The 30th International Conference on International Conference on Machine Learning, Atlanta, USA, 2013: III-1058–III-1066.
    [224]
    QIAN Zhuang, HUANG Kaizhu, WANG Qiufeng, et al. A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies[J]. Pattern Recognition, 2022, 131: 108889. doi: 10.1016/j.patcog.2022.108889.
    [225]
    FREDRIKSON M, JHA S, and RISTENPART T. Model inversion attacks that exploit confidence information and basic countermeasures[C]. The 22nd ACM SIGSAC Conference on Computer and Communications Security, Denver, USA, 2015: 1322–1333. doi: 10.1145/2810103.2813677.
    [226]
    CHEN Huajie, LIU Chi, ZHU Tianqing, et al. When deep learning meets watermarking: A survey of application, attacks and defenses[J]. Computer Standards & Interfaces, 2024, 89: 103830. doi: 10.1016/j.csi.2023.103830.
    [227]
    DENG Lei, LI Guoqi, HAN Song, et al. Model compression and hardware acceleration for neural networks: A comprehensive survey[J]. Proceedings of the IEEE, 2020, 108(4): 485–532. doi: 10.1109/JPROC.2020.2976475.
    [228]
    GUO Sensen, LI Xiaoyu, ZHU Peican, et al. ADS-detector: An attention-based dual stream adversarial example detection method[J]. Knowledge-Based Systems, 2023, 265: 110388. doi: 10.1016/j.knosys.2023.110388.
    [229]
    SCHIOPPA A, FILIPPOVA K, TITOV I, et al. Theoretical and practical perspectives on what influence functions do[C]. The Thirty-Seventh Annual Conference on Neural Information Processing Systems, New Orleans, USA, 2023.
    [230]
    EU Commission. EU Commission—Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts, COM/2021/206 final[S]. 2021.
    [231]
    ZENG Leya, WANG Buhong, TIAN Jiwei, et al. Threat impact analysis to air traffic control systems through flight delay modeling[J]. Computers & Industrial Engineering, 2021, 162: 107731. doi: 10.1016/j.cie.2021.107731.
    [232]
    ZENG Leya, WANG Buhong, WANG Tianrui, et al. Research on delay propagation mechanism of air traffic control system based on causal inference[J]. Transportation Research Part C: Emerging Technologies, 2022, 138: 103622. doi: 10.1016/j.trc.2022.103622.
    [233]
    白洁, 王布宏, 田继伟, 等. 基于动态离散模型的机场ADS-B攻击威胁影响研究[J]. 电光与控制, 2024, 31(7): 27–35, 52. doi: 10.3969/j.issn.1671-637X.2024.07.005.

    BAI Jie, WANG Buhong, TIAN Jiwei, et al. On threat impact of airport ADS-B attack based on dynamic discrete model[J]. Electronics Optics & Control, 2024, 31(7): 27–35, 52. doi: 10.3969/j.issn.1671-637X.2024.07.005.
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