An analytical review of relevant scientific publications in the field of detecting abnormal user activity when working with information systems is conducted. Behavioral analysis in combination with machine and deep learning algorithms opens up new opportunities for early detection of insider threats.
Methods for improving the effectiveness of countering insiders in information systems are analyzed by building an adequate model of the abnormal behavioral profile of users of the customer relationship management system.
The article substantiates the feasibility of an approach to detecting insiders in a computer network based on the use of machine learning methods and big data processing, which allows for the consideration of a variety of parameters that are not directly related to each other, as well as the automation of this process.

Keywords: information systems, information security, insider, abnormal activity, behavioral profile, cluster neighborhood

This study examines the structure and characteristics of multilayer autoencoders (MAEs) used in detecting computer attacks. The potential of MAEs for improving detection capabilities in cybersecurity is analyzed, with a focus on their role in reducing the dimensionality of large datasets involved in identifying computer attacks. The study explores the use of different neuron activation functions within the network and the most commonly applied loss functions that define reconstruction quality of the original data. Additionally, an optimization algorithm for autoencoder parameters is considered, designed to accelerate model training, reduce the likelihood of overfitting, and minimize the loss function.

Keywords: neural networks, layers, neurons, loss function, activation function, mobile applications, attacks, hyperparameters, optimization, machine learning