Patent · US Active

Method and system for learning representations for log data in cybersecurity

US10367841B2 · kind B2 · utility

2Cited by

5References

17Claims

0Family size

Inventors

Ignacio Arnaldo · San Jose, US
Ankit Arun · San Jose, US
Mei Lam · Austin, US
COSTAS BASSIAS · San Jose, US

Key dates

Filing date	Nov 22, 2017
Grant date	Jul 30, 2019
Priority date	—
Expiry date	Nov 22, 2037

Classification

Technology area (CPC H)Electricity
CPC primaryH04L63/1416
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

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.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.