Automatic dependent surveillance-broadcast (ADS-B) is an important part of the new generation air traffic management system of civil aviation. As the protocol does not have data encryption and authentication, it is vulnerable to data attacks. To accurately detect ADS-B data attacks, based on the timing of ADS-B data, this study proposes a convolutional neural networks-long short-term memory (CNN-LSTM) anomaly detection model based on attention mechanism. Firstly, CNN is adopted to extract the features of ADS-B data, and then the feature vectors are input into the LSTM in the form of time series. Finally, the attention mechanism is applied to optimize the network parameters to realize the prediction of ADS-B data, and the anomaly detection is carried out by calculating the prediction error. Experiments show that the model can well detect four types of abnormal data and has higher accuracy and F1 score than other machine learning methods.