Multidevice False Data Injection Attack Models of ADS-B Multilateration Systems

被引:9
|
作者
Shang, Fute [1 ]
Wang, Buhong [1 ]
Yan, Fuhu [1 ]
Li, Tengyao [1 ]
机构
[1] Air Force Engn Univ, Sch Informat & Nav, Xian, Shaanxi, Peoples R China
关键词
Anomaly detection;
D O I
10.1155/2019/8936784
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Location verification is a promising approach among various ADS-B security mechanisms, which can monitor announced positions in ADS-B messages with estimated positions. Based on common assumption that the attacker is equipped with only a single device, this mechanism can estimate the position state through analysis of time measurements of messages using multilateration algorithm. In this paper, we propose the formal model of multidevice false data injection attacks in the ATC system against the location verification. Assuming that attackers equipped with multiple devices can manipulate the ADS-B messages in distributed receivers without any mutual interference, such attacker can efficiently construct attack vectors to change the results of multilateration. The feasibility of a multidevice false data injection attack is demonstrated experimentally. Compared with previous multidevice attacks, the multidevice false data injection attacks can offer lower cost and more covert attacks. The simulation results show that the proposed attack can reduce the attackers' cost by half and achieve better time synchronization to bypass the existing anomaly detection. Finally, we discuss the real-world constraints that limit their effectiveness and the countermeasures of these attacks.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] Online sequential attack detection for ADS-B data based on hierarchical temporal memory
    Li, Tengyao
    Wang, Buhong
    Shang, Fute
    Tian, Jiwei
    Cao, Kunrui
    COMPUTERS & SECURITY, 2019, 87
  • [22] A Machine Learning Approach for the Detection of Injection Attacks on ADS-B Messaging Systems
    Price, Joshua
    Slimane, Hadjar Ould
    Al Shamaileh, Khair
    Devabhaktuni, Vijay
    Kaabouch, Naima
    2023 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS, ICNC, 2023, : 293 - 297
  • [23] ADS-B System Failure Modes and Models
    Ali, Busyairah Syd
    Ochieng, Washington
    Majumdar, Arnab
    Schuster, Wolfgang
    Chiew, Thiam Kian
    JOURNAL OF NAVIGATION, 2014, 67 (06): : 995 - 1017
  • [24] False Data Injection Attack and Corresponding Countermeasure in Multienergy Systems
    Zhang, Qiwei
    Li, Fangxing
    Zhao, Jin
    She, Buxin
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2024, 39 (02) : 3537 - 3547
  • [25] On False Data Injection Attack against Building Automation Systems
    Cash, Michael
    Morales-Gonzalez, Christopher
    Wang, Shan
    Jin, Xipeng
    Parlato, Alex
    Zhu, Jason
    Sun, Qun Zhou
    Fu, Xinwen
    2023 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS, ICNC, 2023, : 35 - 41
  • [26] ADS-BI: Compressed Indexing of ADS-B Data
    Wandelt, Sebastian
    Sun, Xiaoqian
    Fricke, Hartmut
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2018, 19 (12) : 3795 - 3806
  • [27] Detecting Injection Attacks in ADS-B Devices Using RNN-Based Models
    Khoei, Tala Talaei
    Slimane, Hadjar Ould
    Al Shamaileh, Khair
    Devabhaktuni, Vijaya Kumar
    Kaabouch, Naima
    2024 INTEGRATED COMMUNICATIONS, NAVIGATION AND SURVEILLANCE CONFERENCE, ICNS, 2024,
  • [28] ADS-B Attack Classification using Machine Learning Techniques
    Kacem, Thabet
    Kaya, Aydin
    Keceli, Ali Seydi
    Catal, Cagatay
    Wijsekera, Duminda
    Costa, Paulo
    2021 IEEE INTELLIGENT VEHICLES SYMPOSIUM WORKSHOPS (IV WORKSHOPS), 2021, : 7 - 12
  • [29] ADS-B spoofing attack detection method based on LSTM
    Wang, Jing
    Zou, Yunkai
    Ding, Jianli
    EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2020, 2020 (01)
  • [30] ADS-B spoofing attack detection method based on LSTM
    Jing Wang
    Yunkai Zou
    Jianli Ding
    EURASIP Journal on Wireless Communications and Networking, 2020