The automatic dependent surveillance-broadcast (ADS-B) represents a major change in flight tracking and it is one of the key components in building the next generation of air transportation systems. However, several concerns have been raised regarding its vulnerabilities to cyber attacks. In recent years, a new and promising approach of utilizing large-scale and publicly available flight recordings for training machine learning models that can detect anomalous flight patterns has been demonstrated as a valid countermeasure for several ADS-B attacks. The new approach differs significantly from previously proposed methods in the simplicity of its integration with the current ADS-B system. It also provides a valid countermeasure against highly sophisticated airborne attackers. However, previously proposed machine learning methods require training a different model for each flight route or geographic location to give acceptable results. This requirement limits the current solution to flights with a sufficient amount of historical data, which is unavailable in many cases such as business aviation, instructional flying, aerial work, and more. In this research, we address this limitation of previous work, by applying a differencing time-series transformation on the ADS-B data and utilizing a non-recurrent autoencoder classifier. The effectiveness of our method is compared to existing methods on several simulated trajectory modification attacks. The results of our experiments show that the proposed method achieves a ROC AUC value of 0.935-0.951, in comparison to 0.627 from existing methods when evaluated on flights that are absent from training data.

自动相关监视广播 (ADS-B) 代表了飞行跟踪的重大变化，它是构建下一代航空运输系统的关键组成部分之一。然而，人们对其易受网络攻击的脆弱性提出了一些担忧。近年来，一种新的、有前途的方法利用大规模和公开可用的飞行记录来训练可以检测异常飞行模式的机器学习模型，已被证明是针对几种 ADS-B 攻击的有效对策。新方法在与当前 ADS-B 系统集成的简单性方面与以前提出的方法有很大不同。它还针对高度复杂的空中攻击者提供了有效的对策。然而，之前提出的机器学习方法需要为每个飞行路线或地理位置训练不同的模型，以给出可接受的结果。此要求将当前解决方案限制在具有足够历史数据量的航班上，而这在许多情况下是不可用的，例如公务航空、教学飞行、空中作业等。在这项研究中，我们通过对 ADS-B 数据应用差分时间序列转换并利用非循环自动编码器分类器来解决先前工作的这一局限性。在几种模拟轨迹修改攻击上，我们的方法与现有方法的有效性进行了比较。我们的实验结果表明，与 0 相比，所提出的方法实现了 0.935-0.951 的 ROC AUC 值。