Research on ADS-B Attack Classification Based on Transfer Learning-Meta Learning
Accurately categorizing the types of ADS-B attacks in the field of navigation and taking pre-ventive measures against the attacks are of great significance to guarantee the security of navigation opera-tion.Aiming at the small number of ADS-B attack data samples in the field of navigation,an ADS-B at-tack diagnosis and classification model based on migration learning-meta-learning is proposed.The mod-el combines migration learning with deep convolutional self-encoder to build an ADS-B attack feature model,extracts the effective feature representation of the attack from the data samples,and applies a meta-learning strategy to achieve accurate classification of ADS-B attacks in the feature space.An example study shows that the attack diagnosis classification model based on migration learning-meta-learning can effectively classify small-sample ADS-B attacks with more than 95％correct rate.
transfer learningconvolutional auto encodermodel-agnostic meta-learningADS-B inter-ference classificationgeneral aviation
万方数据
维普
