Deep Learning for Analyzing User and Entity Behaviors: Techniques and Applications

Abstract

User and entity behavior analytics (UEBA) is a critical component of modern cybersecurity strategies aimed at detecting and mitigating security threats within enterprise environments. The system is designed to enhance security through the analysis of user and entity actions. The architecture encompasses data collection and integration techniques, feature extraction, deep learning models, detecting, and analyzing. Data collection strategies acquire statistics from quite a few sources, such as system logs, network site visitors, and application logs, resulting in a comprehensive dataset for analysis. Feature extraction transforms uncooked facts right into a meaningful representation, for reading a deep modelling can locate patterns in person conduct and entity conduct. Long-term and brief-term reminiscence (LSTM) and convolutional LSTM (ConvLSTM) fashions are used to investigate temporal, spatial, and temporal dependence, respectively. It detects irregularities and makes immediate corrections.