Abstract For the next generation air traffic surveillance, ADS-B is becoming the primary method to obtain more accurate data with wide coverage, which establishes the foundation for automatic and intelligent air traffic management system. However, ADS-B is designed without sufficient security considerations, transmitting with plain text without integrity and authentication validations. Thus, ADS-B data is in face of various attack threats, which may cause disruptions on system availability and reliability. To eliminate effects of attack behaviours, attack detection is in demand to avoid attack data injecting into decision making flow. Based on hidden Markov model with sticky hierarchical Dirichlet process, the dynamic temporal detection method is proposed to detect multiple attack patterns. Taking advantage of multiple attribute data, the dimensions of data are reduced to one dimension to set up feature sequences. With sticky hierarchical Dirichlet process, the parameters are obtained for hidden Markov model dynamically. Utilizing hidden Markov model, the generative model is established to predict hidden states of ADS-B data sequence. By analysing the contextual deviation information on hidden state sequences, the attack behaviours are discriminated to determine the attack data. By experiments on real ADS-B data, the feasibility and accuracy of the proposed method are validated.

中文翻译：

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基于sHDP-HMM的动态时间ADS-B数据攻击检测

摘要 对于下一代空中交通监视，ADS-B正在成为获取更准确、覆盖范围更广的数据的主要手段，为空中交通管理系统的自动化、智能化奠定了基础。然而，ADS-B 的设计没有足够的安全考虑，以纯文本传输，没有完整性和身份验证验证。因此，ADS-B 数据面临各种攻击威胁，可能会导致系统可用性和可靠性的中断。为了消除攻击行为的影响，需要进行攻击检测以避免攻击数据注入决策流程。基于具有粘性分层Dirichlet过程的隐马尔可夫模型，提出了动态时间检测方法来检测多种攻击模式。利用多属性数据，将数据的维度降为一维以建立特征序列。采用粘性分层Dirichlet过程，动态获取隐马尔可夫模型的参数。利用隐马尔可夫模型，建立生成模型来预测ADS-B数据序列的隐状态。通过分析隐藏状态序列的上下文偏差信息，区分攻击行为以确定攻击数据。通过对真实ADS-B数据的实验，验证了所提方法的可行性和准确性。通过分析隐藏状态序列的上下文偏差信息，区分攻击行为以确定攻击数据。通过对真实ADS-B数据的实验，验证了所提方法的可行性和准确性。通过分析隐藏状态序列的上下文偏差信息，区分攻击行为以确定攻击数据。通过对真实ADS-B数据的实验，验证了所提方法的可行性和准确性。