ADS-B攻擊數(shù)據(jù)彈性恢復方法

李騰耀; 王布宏; 尚福特; 田繼偉; 曹堃銳

doi:10.11999/JEIT191020

ADS-B攻擊數(shù)據(jù)彈性恢復方法

doi: 10.11999/JEIT191020 cstr: 32379.14.JEIT191020

李騰耀¹,
王布宏^1, ,,
尚福特¹,
田繼偉¹,
曹堃銳^{1, 2}

1.
空軍工程大學信息與導航學院西安 710077
2.
國防科技大學信息通信學院西安 710106

基金項目: 國家自然科學基金(61902426)

詳細信息

作者簡介:
李騰耀：男，1991年生，博士生，研究方向為ADS-B數(shù)據(jù)攻擊檢測及彈性恢復

王布宏：男，1975年生，博士，教授，研究方向為空管安全、信息物理系統(tǒng)安全和人工智能安全

尚福特：男，1992年生，博士生，研究方向為信息物理系統(tǒng)安全

田繼偉：男，1993年生，博士生，研究方向為信息物理系統(tǒng)安全和人工智能安全

曹堃銳：男，1989年生，博士生，研究方向為無線網(wǎng)絡(luò)物理層安全

通訊作者:
王布宏　wbhgroup@aliyun.com

中圖分類號: TP915.08
計量
- 文章訪問數(shù): 1789
- HTML全文瀏覽量: 652
- PDF下載量: 75
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-12-23
- 修回日期: 2020-05-22
- 網(wǎng)絡(luò)出版日期: 2020-07-21
- 刊出日期: 2020-10-13

A Resilient Recovery Method on ADS-B Attack Data

1.
College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
2.
School of Information and Communications, National University of Defense Technology, Xi’an 710106, China

Funds: The National Natural Science Foundation of China (61902426)

摘要

摘要: 為了對自動廣播相關(guān)監(jiān)視(ADS-B)攻擊數(shù)據(jù)進行彈性恢復，確保空情態(tài)勢感知信息的持續(xù)可用性，該文提出針對ADS-B攻擊數(shù)據(jù)的彈性恢復方法。基于前置的攻擊檢測機制，獲取當前ADS-B量測數(shù)據(jù)序列和預測數(shù)據(jù)序列，并在此基礎(chǔ)上構(gòu)建偏差數(shù)據(jù)序列、差分數(shù)據(jù)序列和鄰近密度數(shù)據(jù)序列。依托偏差數(shù)據(jù)構(gòu)建恢復向量，依托差分數(shù)據(jù)挖掘攻擊數(shù)據(jù)的時序特性，依托鄰近密度數(shù)據(jù)挖掘攻擊數(shù)據(jù)的空間特性。通過整合3種數(shù)據(jù)序列構(gòu)建彈性恢復策略并確定恢復終止點，實現(xiàn)對攻擊影響的弱化，將ADS-B攻擊數(shù)據(jù)向正常數(shù)據(jù)方向進行定向恢復。通過對6種典型攻擊樣式的實驗分析，證明該彈性恢復方法能夠有效恢復ADS-B攻擊數(shù)據(jù)，削弱數(shù)據(jù)攻擊對監(jiān)視系統(tǒng)的影響。
- 空管監(jiān)視 /
- 自動廣播相關(guān)監(jiān)視 /
- 數(shù)據(jù)安全 /
- 攻擊檢測 /
- 彈性恢復
Abstract: In order to conduct effective resilient recovery on Automatic Dependent Surveillance-Broadcast (ADS-B) attack data and ensure the continuous availability of air traffic situation awareness, a resilient recovery method on ADS-B attack data is proposed. Based on attack detection strategies, the measurement and prediction sequences of ADS-B data are obtained to set up deviation data, differential data and neighbor density data sequences, which are designed to construct recovery vectors, mine the temporal correlations and the spatial correlations respectively. The selected data sequences are integrated to accomplish the whole recovery method and decide the end point of recovery. The method is applied to elinimating attack effects and recovering the attack data towards normal data. According to the results of experiments on six classical attack patterns, the proposed method is effective on recovering attack data and eliminating the attack impacts.
- Air traffic surveillance /
- Automatic Dependent Surveillance-Broadcast (ADS-B) /
- Data security /
- Attack detection /
- Resilient recovery

HTML全文

圖 1 問題建模

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圖 2 偏差數(shù)據(jù)分析

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圖 3 數(shù)據(jù)恢復效果(ATK-1～ATK-3)

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圖 4 差分數(shù)據(jù)分析

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圖 5 數(shù)據(jù)恢復效果(ATK-4, ATK-5)

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圖 6 數(shù)據(jù)恢復效果(ATK-6)

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圖 7 恢復策略效能分析

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表 1 構(gòu)造的典型攻擊樣式

編號	攻擊模式	攻擊影響
ATK-1	常量偏差注入攻擊	針對ADS-B多屬性數(shù)據(jù)注入常量偏差
ATK-2	隨機偏差注入攻擊	針對ADS-B多屬性數(shù)據(jù)注入隨機偏差
ATK-3	增量偏差注入攻擊	針對ADS-B多屬性數(shù)據(jù)注入增量偏差
ATK-4	航跡替換攻擊	針對特定時間窗口內(nèi)的航跡進行替換
ATK-5	航跡重放攻擊	在特定時間長度下實現(xiàn)航跡重放
ATK-6	飛行器泛洪攻擊	向當前空域態(tài)勢中注入大量幽靈飛行器目標

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參考文獻(17)

STROHMEIER M, SCH?FER 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

WANG Wenyi, WU Renbiao, and LIANG Junli. ADS-B signal separation based on blind adaptive beamforming[J]. IEEE Transactions on Vehicular Technology, 2019, 68(7): 6547–6556. doi: 10.1109/TVT.2019.2914233

SUN Junzi, V? H, ELLERBROEK J, et al. pyModeS: Decoding mode-S surveillance data for open air transportation research[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(7): 2777–2786. doi: 10.1109/TITS.2019.2914770

STROHMEIER M, SCH?FER M, LENDERS V, et al. Realities and challenges of NextGen air traffic management: The case of ADS-B[J]. IEEE Communications Magazine, 2014, 52(5): 111–118. doi: 10.1109/MCOM.2014.6815901

STROHMEIER M, LENDERS V, and MARTINOVIC I. On the security of the automatic dependent surveillance-broadcast protocol[J]. IEEE Communications Surveys & Tutorials, 2015, 17(2): 1066–1087. doi: 10.1109/comst.2014.2365951

MANESH M R and KAABOUCH N. Analysis of vulnerabilities, attacks, countermeasures and overall risk of the automatic dependent surveillance-broadcast (ADS-B) system[J]. International Journal of Critical Infrastructure Protection, 2017, 19: 16–31. doi: 10.1016/j.ijcip.2017.10.002

錢亞冠, 盧紅波, 紀守領(lǐng), 等. 基于粒子群優(yōu)化的對抗樣本生成算法[J]. 電子與信息學報, 2019, 41(7): 1658–1665. doi: 10.11999/JEIT180777

QIAN Yaguan, LU Hongbo, JI Shouling, et al. Adversarial example generation based on particle swarm optimization[J]. Journal of Electronics &Information Technology, 2019, 41(7): 1658–1665. doi: 10.11999/JEIT180777

SCH?FER M, LENDERS V, and MARTINOVIC I. Experimental analysis of attacks on next generation air traffic communication[C]. The 11th International Conference on Applied Cryptography and Network Security, Berlin, Germany, 2013: 253–271. doi: 10.1007/978-3-642-38980-1_16.

COSTIN A and FRANCILLON A. Ghost in the air (traffic): On insecurity of ADS-B protocol and practical attacks on ADS-B devices[C]. Black Hat, Las Vegas, USA, 2012: 1–10.

陳紅松, 陳京九. 基于循環(huán)神經(jīng)網(wǎng)絡(luò)的無線網(wǎng)絡(luò)入侵檢測分類模型構(gòu)建與優(yōu)化研究[J]. 電子與信息學報, 2019, 41(6): 1427–1433. doi: 10.11999/JEIT180691

CHEN Hongsong and CHEN Jingjiu. Recurrent neural networks based wireless network intrusion detection and classification model construction and optimization[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1427–1433. doi: 10.11999/JEIT180691

YING Xuhang, MAZER J, BERNIERI G, et al. Detecting ADS-B spoofing attacks using deep neural networks[C]. 2019 IEEE Conference on Communications and Network Security, Washington, USA, 2019: 187–195. doi: 10.1109/CNS.2019.8802732.

HABLER E and SHABTAI A. Using LSTM encoder-decoder algorithm for detecting anomalous ADS-B messages[J]. Computers & Security, 2018, 78: 155–173. doi: 10.1016/j.cose.2018.07.004

丁建立, 鄒云開, 王靜, 等. 基于深度學習的ADS-B異常數(shù)據(jù)檢測模型[J]. 航空學報, 2019, 40(12): 323220. doi: 10.7527/S1000-6893.2019.23220

DING Jianli, ZOU Yunkai, WANG Jing, et al. ADS-B anomaly data detection model based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(12): 323220. doi: 10.7527/S1000-6893.2019.23220

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

ZHANG Tao, WU Renbiao, LAI Ran, et al. Probability hypothesis density filter for radar systematic bias estimation aided by ADS-B[J]. Signal Processing, 2016, 120: 280–287. doi: 10.1016/j.sigpro.2015.09.012

SMITH M, STROHMEIER M, HARMAN J, et al. Safety vs. security: Attacking avionic systems with humans in the loop[J]. arXiv, 2019, 1905.08039.

STROHMEIER M, MARTINOVIC I, FUCHS M, et al. Opensky: A swiss army knife for air traffic security research[C]. The 34th IEEE/AIAA Digital Avionics Systems Conference, Prague, Czech Republic, 2015: 1–14. doi: 10.1109/DASC.2015.7311411.

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