Modern Defense Technology ›› 2022, Vol. 50 ›› Issue (5): 28-35.DOI: 10.3969/j.issn.1009-086x.2022.05.005
• AIR SPACE DEFENSE SYSTEM AND WEAPON • Previous Articles Next Articles
Jia-wei BAO, Xiao-ping TIAN, Yu-na LIU, Chang-kuan ZOU, Yu-qing ZHANG
Received:
2021-11-15
Revised:
2022-02-22
Online:
2022-10-28
Published:
2022-11-03
作者简介:
暴佳伟(1994-),男,山西太原人。硕士生,研究方向为ADS-B信号的识别,数据的处理。
基金资助:
CLC Number:
Jia-wei BAO, Xiao-ping TIAN, Yu-na LIU, Chang-kuan ZOU, Yu-qing ZHANG. Summary of Research on Security Issues of ADS-B Protocol[J]. Modern Defense Technology, 2022, 50(5): 28-35.
暴佳伟, 田小平, 刘宇娜, 邹长宽, 张雨晴. ADS-B安全问题研究综述[J]. 现代防御技术, 2022, 50(5): 28-35.
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Bit 1-5 | 6-8 | 9-32 | 33-88 | 89-112 |
---|---|---|---|---|
DF=17 | CA | AA | ME字段(ADS-B消息) | PI |
DF=18 | CF=0 | AA | ME字段(ADS-B消息) | PI |
CF=1 | AA | ME字段(ADS-B消息) | PI | |
CF=2-3 | AA | ME字段(TIS-B消息) | PI | |
CF=4 | 为TIS-B消息预留位 | PI | ||
CF=5 | AA | ME字段(TIS-B消息) | PI | |
CF=6-7 | 预留位 | PI | ||
DF=19 | AF=0 | AA | ME字段(ADS-B消息) | PI |
AF=1-7 | 为军事应用预留位 | PI |
Table 1 ADS-B message format structure[22]
Bit 1-5 | 6-8 | 9-32 | 33-88 | 89-112 |
---|---|---|---|---|
DF=17 | CA | AA | ME字段(ADS-B消息) | PI |
DF=18 | CF=0 | AA | ME字段(ADS-B消息) | PI |
CF=1 | AA | ME字段(ADS-B消息) | PI | |
CF=2-3 | AA | ME字段(TIS-B消息) | PI | |
CF=4 | 为TIS-B消息预留位 | PI | ||
CF=5 | AA | ME字段(TIS-B消息) | PI | |
CF=6-7 | 预留位 | PI | ||
DF=19 | AF=0 | AA | ME字段(ADS-B消息) | PI |
AF=1-7 | 为军事应用预留位 | PI |
类型 | 方法 | 要求 | 优点 | 缺点 |
---|---|---|---|---|
ADS-B传输过程 | 加密 | 需要ADS-B 消息帧内有空间来存储加密数据或哈希值 | 对数据链路进行了保护 | 成本高,实施难度大 |
ADS-B硬件和软件 | 阵列天线改进 | 依赖地面站和其他实体 | 提高信号的检测强度 | 对天线质量和位置精度要求高,检测不稳定 |
接收系统改进 | 依赖接收装置 | 提高抗干扰能力 | 设备成本高,开发周期长 | |
ADS-B数据 | 机器 学习 | 需要处理过的数据集 | 有效识别异常出数据 | 不适用于大规模的数据集开发 |
深度 学习 | 需要大量的数据集 | 开发时间少,提高了ADS-B异常信号检测效率 | 数据集的好快决定检测程度,计算成本高 |
Table 2 ADS-B security problem method comparison
类型 | 方法 | 要求 | 优点 | 缺点 |
---|---|---|---|---|
ADS-B传输过程 | 加密 | 需要ADS-B 消息帧内有空间来存储加密数据或哈希值 | 对数据链路进行了保护 | 成本高,实施难度大 |
ADS-B硬件和软件 | 阵列天线改进 | 依赖地面站和其他实体 | 提高信号的检测强度 | 对天线质量和位置精度要求高,检测不稳定 |
接收系统改进 | 依赖接收装置 | 提高抗干扰能力 | 设备成本高,开发周期长 | |
ADS-B数据 | 机器 学习 | 需要处理过的数据集 | 有效识别异常出数据 | 不适用于大规模的数据集开发 |
深度 学习 | 需要大量的数据集 | 开发时间少,提高了ADS-B异常信号检测效率 | 数据集的好快决定检测程度,计算成本高 |
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