Leveraging Deep Learning Techniques for User and Entity Behavior Analysis

Entity Behavior Analysis of user is a key of modern cyber security solutions that helps identify and reduce agency threats. The generation studies the behavior of organizations and consumers in an effort to increase security. Information gathering and integration methodologies, function extraction, deep learning models, search, and assessment are some of the techniques. In order to provide a comprehensive dataset for analysis, data collecting strategies gather statistics from a variety of sources, encompassing application logs, network site visits, and system logs. Deep modelling may find patterns in entity and human behavior by using feature extraction to raw data transform into a understandable representation. The Long Short-Term Memory (LSTM) and convolutional LSTM (ConvLSTM) models used for analyses temporal, spatial, and temporal dependency. Any irregularities it detects are immediately fixed.