NEURAL NETWORK METHOD OF USER BEHAVIOR ANALYSIS BASED ON KEYSTROKE DYNAMICS
Abstract
The reason for most of the information leaks is the compromise of account data. In this regard, the introduction of additional means of identification and authentication is relevant. To increase efficiency, these systems are developed using machine learning. The use of neural networks is currently the most promising approach to improving the security of systems due to their speed and accuracy. This paper discusses the use of keystroke dynamics to identify authorized users. Artificial neural networks are used to analyze the dynamics of pressing. In this paper, such characteristics as the time of pressing a key, the time between keystrokes, the time between releasing the first key and pressing the second were analyzed. Both convolutional and recurrent neural networks were used. The primary processing of input data was carried out by a sliding window that formed data blocks of a certain size. For further processing of already structured data, a onedimensional convolutional neural network was chosen, since it is well suited for processing data presented in the form of a sequence. A recurrent neural network, namely the LSTM architecture, was used to process time dependencies, since it processes variable-length sequences best and is less susceptible to gradient decay and explosion than others. For experimental verification of the effectiveness of this technique, the following architectures were implemented: 2xLSTM, 1D SNC + LSTM, 1D SNC + 2xLSTM. Based on the results of model training, it was revealed that the system based on the 1D SNC + 2xLSTM architecture with a sliding window size of 50 has the highest accuracy. The validation accuracy of this architecture was 98.29%. ROC curves were constructed, which confirmed the effectiveness of this architecture. The F-measure was calculated, which showed that the highest performance of binary classification is achieved when using the 1D SNC + 2xLSTM architecture with a sliding window size of 50 and equal to 99.39%.
