基于注意力机制的CNN-LSTM的ADS-B异常数据检测

ADS-B Anomaly Detection Based on Attention Mechanism for CNN-LSTM

广播式自动相关监视(ADS-B)是民航新一代空中交通管理系统的重要组成部分,由于协议没有数据加密和认证,导致容易受到数据攻击.为了准确检测ADS-B数据攻击,基于ADS-B数据的时序性,提出了一种基于注意力机制的卷积神经网络-长短期记忆网络(convolutional neural networks-long short-term memory,CNN-LSTM)异常数据检测模型.首先,利用CNN提取ADS-B数据的特征,然后以时序形式将特征向量输入到LSTM中,最后使用注意力机制进行网络参数优化,实现对ADS-B数据的预测,通过计算预测误差,来进行异常检测.实验表明,该模型能够很好地检测出模拟的4种类型的异常数据,与其他机器学习方法相比,具有更高的准确率和F1分数.

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.