The invention discloses a network intrusion detection method based on a conditional variation auto-encoder, belongs to the technical field of network space intrusion detection, and the method comprises the steps: firstly training a conditional variation auto-encoder which takes a logarithmic hyperbolic cosine function as a loss function for the expansion of a data set, and then training a classifier based on the convolutional neural network through the data set after data expansion to serve as an intrusion detector of the whole model. According to the method, the classification detection performance on a network space intrusion data set is improved by utilizing the generation capacity of conditional variation self-encoding and the excellent feature extraction capacity of the convolutionalnetwork for data features, and meanwhile, the problem of performance reduction caused by imbalance of data set samples is also solved.

Network intrusion detection method based on conditional variation auto-encoder

The invention discloses a human body activity identification system and method based on a conditional variational auto-encoder, and the method comprises the steps: obtaining an original time sequence:obtaining sampling samples through a sensor, and enabling a plurality of sampling samples to form the original time sequence; constructing batch data: constructing the batch data in a data enhancement mode of a random sequence starting point to obtain constructed sensor batch data X and corresponding activity label batch data Y; training a condition variation auto-encoder model: inputting batch data into the model, and training the model through a loss function and a back propagation algorithm; predicting human body activities: inputting the sensor batch data X as test data into the trained variational auto-encoder model, and inputting the batch data into the variational auto-encoder model to obtain a final prediction activity tag. According to the method, one sampling sample is used as aunit to predict the corresponding activity label, the real-time activity recognition capability is achieved, modeling can be conducted on the correlation of similar samples, and therefore the recognition accuracy is improved.

Human body activity recognition system and method based on conditional variational auto-encoder

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Human body activity recognition system and method based on conditional variational auto-encoder