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The purpose of the automatic dependent surveillance broadcast (ADS-B)technology is to serve as a replacement for the current radar-based, airtraffic control systems. Despite the considerable time and resources devoted todesigning and developing the system, the ADS-B is well known for its lack ofsecurity mechanisms. Attempts to address these security vulnerabilities havebeen made in previous studies by modifying the protocol's current architectureor by using additional hardware components. These solutions, however, areconsidered impractical because of 1) the complex regulatory process involvingavionic systems, 2) the high costs of using hardware components, and 3) thefact that the ADS-B system itself is already deployed in most aircraft andground stations around the world. In this paper, we propose VizADS-B, analternative software-based security solution for detecting anomalous ADS-Bmessages, which does not require any alteration of the current ADS-Barchitecture or the addition of sensors. According to the proposed method, theinformation obtained from all aircraft within a specific geographical area isaggregated and represented as a stream of images. Then, a convolutional LSTMencoder-decoder model is used for analyzing and detecting anomalies in thesequences of images. In addition, we propose an explainability technique,designed specifically for convolutional LSTM encoder-decoder models, which isused for providing operative information to the pilot as a visual indicator ofa detected anomaly, thus allowing the pilot to make relevant decisions. Weevaluated our proposed method on five datasets by injecting and subsequentlyidentifying five different attacks. Our experiments demonstrate that most ofthe attacks can be detected based on spatio-temporal anomaly detectionapproach.

VizADS-B: Analyzing Sequences of ADS-B Images Using Explainable Convolutional LSTM Encoder-Decoder to Detect Cyber Attacks