1 research outputs found
VizADS-B: Analyzing Sequences of ADS-B Images Using Explainable Convolutional LSTM Encoder-Decoder to Detect Cyber Attacks
The purpose of the automatic dependent surveillance broadcast (ADS-B)
technology is to serve as a replacement for the current radar-based, air
traffic control systems. Despite the considerable time and resources devoted to
designing and developing the system, the ADS-B is well known for its lack of
security mechanisms. Attempts to address these security vulnerabilities have
been made in previous studies by modifying the protocol's current architecture
or by using additional hardware components. These solutions, however, are
considered impractical because of 1) the complex regulatory process involving
avionic systems, 2) the high costs of using hardware components, and 3) the
fact that the ADS-B system itself is already deployed in most aircraft and
ground stations around the world. In this paper, we propose VizADS-B, an
alternative software-based security solution for detecting anomalous ADS-B
messages, which does not require any alteration of the current ADS-B
architecture or the addition of sensors. According to the proposed method, the
information obtained from all aircraft within a specific geographical area is
aggregated and represented as a stream of images. Then, a convolutional LSTM
encoder-decoder model is used for analyzing and detecting anomalies in the
sequences of images. In addition, we propose an explainability technique,
designed specifically for convolutional LSTM encoder-decoder models, which is
used for providing operative information to the pilot as a visual indicator of
a detected anomaly, thus allowing the pilot to make relevant decisions. We
evaluated our proposed method on five datasets by injecting and subsequently
identifying five different attacks. Our experiments demonstrate that most of
the attacks can be detected based on spatio-temporal anomaly detection
approach