METHOD AND SYSTEM FOR LEARNING REPRESENTATIONS FOR LOG DATA IN CYBERSECURITY

摘要

Disclosed is a data analysis and cybersecurity method, which forms a time-based series of behavioral features, and analyzes the series of behavioral features for attack detection, new features derivation, and/or features evaluation. Analyzing the time based series of behavioral features may comprise using a Feed-Forward Neural Networks (FFNN) method, a Convolutional Neural Networks (CNN) method, a Recurrent Neural Networks (RNN) method, a Long Short-Term Memories (LSTMs) method, a principal Component Analysis (PCA) method, a Random Forest pipeline method, and/or an autoencoder method. In one embodiment, the behavioral features of the time-based series of behavioral features comprise human engineered features, and/or machined learned features, wherein the method may be used to learn new features from historic features.