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.

自动相关监视-广播（ADS-B）监视被视为下一代空中交通管理中的核心技术。由于没有考虑安全性，ADS-B数据在完整性和身份验证方面面临各种挑战，特别是对于具有高隐藏性的ADS-B数据攻击。本文分析了常见的攻击模式模型。在顺序ADS-B数据方面，根据飞行和地面站的能力设计了检测方法，该方法综合了多种检测方法，包括飞行计划验证，单节点数据检测和组数据检测，以生成综合的攻击概率作为判断的参考关于数据攻击。为了提高地面对地面的阳性检测率，提出了空对空和空对空协同检测，以增强每个单节点的检测能力。对实际ADS-B数据进行的实验表明，顺序协同检测策略在有效性和准确性上非常有效，特别是对于随机偏差注入攻击，恒定偏差注入攻击和DoS攻击。