The given paper proposes a method of analyzing network traffic based on recurrent neural networks. There overview of perspective approaches for analyzing network traffic in order to detect attacks is provided. The authors investigated the largest and currently the most relevant CICIDS2018 dataset. The methods of dealing with the class imbalance in a dataset by adapting the Focal Loss function to the problem of traffic analysis are considered. There proposed method provides the effective representation of information characteristics of network packets by means of encoder subnetworks. The resulting embeddings are fed at the input of the recurrent LSTM layer. The designed network meta-architecture is potentially effective for the presented dataset as well as for relevant analogues.

The given article considers the development of a personal identification technique based on the mechanism of scanning and analyzing such biometric parameter as a vein pattern of the palm for automation access control systems. A number of problems characteristic of the existing approaches to solving the given problem have been formulated and the operation analysis of the main ones has been carried out. A mechanism for reading a vein pattern of the palm, as well as three methods for further analysis of the referred biometrics and personal identification: a method based on a categorical classification, a method based on a binary classification, and a combined method have been developed. The resulting architecture of the neural network for the categorical classification of the vein pattern has been built and a method for calculating the number of the model parameters depending on the number of the registered subjects has been obtained. Based on the results of the research, experimental measurements of the system operation accuracy have been made while implementing the mentioned methods. The system based on a binary classification has demonstrated the highest accuracy; however applying a combined approach allows improving the obtained result.

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