Using LSTM encoder-decoder algorithm for detecting anomalous ADS-B messages

Habler, Edan; Shabtai, Asaf

doi:10.1016/j.cose.2018.07.004

Cited by 114 publications

(68 citation statements)

References 19 publications

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“…By analyzing the ADS-B attacks, we construct a neural network made up of LSTM units to detect different types of anomalous data we simulate. Compared with the existing machine learning methods [14,15], our method does not require complicated feature engineering.…”

Section: Introductionmentioning

confidence: 99%

ADS-B spoofing attack detection method based on LSTM

Wang

Zou

Ding

2020

Preprint

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The open and shared nature of the ADS-B protocol makes its messages extremely vulnerable to various security threats, such as jamming, modification, and injection. This paper proposes an LSTM-based ADS-B spoofing attack detection method from the perspective of data. First, the message sequence is preprocessed in the form of a sliding window, and then a long short-term memory (LSTM) network is used to perform prediction training on the windows. Finally, the residual set of predicted values and true values is calculated to set a threshold. As a result, we can detect a spoofing attack and further identify which feature was attacked. Experiments show that this method can effectively detect 10 different kinds of simulated manipulated ADS-B messages without further increasing the complexity of airborne applications. Therefore, the method can respond well to the security threats suffered by the ADS-B system.

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Section: Introductionmentioning

confidence: 99%

ADS-B spoofing attack detection method based on LSTM

Wang

Zou

Ding

2020

Preprint

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“…(5) From the perspective of data, a machine learning method is used to reconstruct the ADS-B message sequence, and the reconstruction error is used to detect anomalous messages. Based on the original features contained in an ADS-B message, Habler et al calculated the distances from all points on the track to four special nodes and the distances between two adjacent track points, for a total of 5 parameters, as additional training features to perform anomaly detection [14]. Our research group statistically expands the original features based on the strong temporal correlation of ADS-B messages so that the model can better capture the time dependence of the data [15].…”

Section: Research Statusmentioning

confidence: 99%

“…Compared with the existing machine learning methods [14,15], our method does not require complicated feature engineering. 2 We set different thresholds for different features, so that we can determine the specific features containing anomalies.…”

Section: Introductionmentioning

confidence: 99%

ADS-B spoofing attack detection method based on LSTM

Wang

Zou

Ding

2020

J Wireless Com Network

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The open and shared nature of the Automatic Dependent Surveillance Broadcast (ADS-B) protocol makes its messages extremely vulnerable to various security threats, such as jamming, modification, and injection. This paper proposes a long short-term memory (LSTM)-based ADS-B spoofing attack detection method from the perspective of data. First, the message sequence is preprocessed in the form of a sliding window, and then, an LSTM network is used to perform prediction training on the windows. Finally, the residual set of predicted values and true values is calculated to set a threshold. As a result, we can detect a spoofing attack and further identify which feature was attacked. Experiments show that this method can effectively detect 10 different kinds of simulated manipulated ADS-B messages without further increasing the complexity of airborne applications. Therefore, the method can respond well to the security threats suffered by the ADS-B system.

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“…It is also interesting to note that some approaches use RNN as part of an autoencoder (e.g., LSTM encoder-decoder (LSTM-ED) [63,78]) or generative architecture (e.g., GAN-AD [68]). See Sections 3.3 and 3.4 for further details.…”

Section: Recent Advances In Recurrent Neural Networkmentioning

confidence: 99%

Recent Advances in Anomaly Detection Methods Applied to Aviation

2019

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Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

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Using LSTM encoder-decoder algorithm for detecting anomalous ADS-B messages

Cited by 114 publications

References 19 publications

ADS-B spoofing attack detection method based on LSTM

ADS-B spoofing attack detection method based on LSTM

ADS-B spoofing attack detection method based on LSTM

Recent Advances in Anomaly Detection Methods Applied to Aviation

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