An aperiodic feature CRED algorithm for ship spoofing detection
To address the issue of ship positioning being solely dependent on the Global Navigation Satellite System(GNSS)and its vulnerability for spoofing attacks,this paper proposes a Non-Periodic Feature Convolutional Recurrent Encoder-Decoder(CRED)algorithm for ship navigation spoofing detection.The covariance matrix of non-periodic feature data from sensors such as GNSS,compass,and log is used as input.A three-layer neural network is constructed using autoencoders,convolutional long-term and short-term memory networks,and attention mechanisms.By accumulating the residual matrices and adding the values of correlated elements in the input matrix,the detection statistic is calculated to identify spoofing attacks.Experiments show that the method performs well in detecting spoofing under various degrees of deception and different sailing states of the ship,including jump attacks and slow attacks.This algorithm can process multi-source non-periodic feature data for ship navigation and exhibits good detection performance and strong generalization capabilities in ship navigation spoofing detection,demonstrating significant practical value.
万方数据
维普
