Biblio
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2020. Application of the Neural Networks for Cryptographic Information Security. 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT QM IS). :421–423.
The object of research is information security. The tools used for research are artificial neural networks. The goal is to increase the cryptography security. The problems are: the big volume of information, the expenses for neural networks design and training. It is offered to use the neural network for the cryptographic transformation of information.
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2018. Constructing Browser Fingerprint Tracking Chain Based on LSTM Model. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). :213-218.
Web attacks have increased rapidly in recent years. However, traditional methods are useless to track web attackers. Browser fingerprint, as a stateless tracking technique, can be used to solve this problem. Given browser fingerprint changes easily and frequently, it is easy to lose track. Therefore, we need to improve the stability of browser fingerprint by linking the new one to the previous chain. In this paper, we propose LSTM model to learn the potential relationship of browser fingerprint evolution. In addition, we adjust the input feature vector to time series and construct training set to train the model. The results show that our model can construct the tracking chain perfectly well with average ownership up to 99.3%.