The article examines the characteristics of insider threats, analyzes typical insider motivations, and identifies the main technical vectors used to carry out attacks, including unauthorized data copying, the use of cloud services, instant messengers, and remote access mechanisms. Particular attention is paid to the role of access control systems in preventing personal data leaks, as well as to contemporary scientific and practical approaches to countering insider activity. It is demonstrated that effective minimization of the risks associated with insider incidents is achievable only through a comprehensive combination of organizational, technical, and legal measures, along with systematic enhancement of personnel awareness in the field of information security.

Keywords: insider threats, personal data leakage, access control, dynamic access control, behavioral analysis

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