Quantitative unified analytic neural networks
AssigneeCertis Cisco Security Pte Ltd
InventorKeng Leng Albert LIM
Life of this patent
Estimating the term…
Abstract
This document describes a system and method for quantitatively unifying and assimilating all unstructured, unlabelled and/or fragmented real-time and non-real-time cyber threat data generated by a plurality of sources. These sources may include cyber-security surveillance systems that are equipped with machine learning capabilities.
Claims22
A method for quantitative unified analysis of unstructured threat intelligence data, the method to be performed by a computer system comprising:
- receiving unstructured threat intelligence data from molecular level sources, wherein the unstructured threat intelligence data comprises security events generated from the molecular level sources;
- uniting the received unstructured threat intelligence data by translating the received threat intelligence data into a uniformed meta-format and storing the translated data in a database, wherein each translated data includes at least a time component;
- determining isolated data points from the translated data stored in the database by grouping the translated data into groups, wherein each group represents a unique time period, and applying unsupervised machine learning techniques to each group of the translated data;
- determining data outliers from the isolated data points;
- and
- generating a learned function using the data outliers.
A computer system for quantitative unified analysis of unstructured threat intelligence data, the method to be performed by a computer system comprising:
- circuitry configured to receive unstructured threat intelligence data from molecular level sources, wherein the unstructured threat intelligence data comprises security events generated from the molecular level sources;
- circuitry configured to unite the received unstructured threat intelligence data by translating the received threat intelligence data into a uniformed meta-format and storing the translated data in a database, wherein each translated data includes at least a time component;
- circuitry configured to determine isolated data points from the translated data stored in the database by grouping the translated data into groups, wherein each group represents a unique time period, and applying unsupervised machine learning techniques to each group of the translated data;
- circuitry configured to determine data outliers from the isolated data points;
- and
- circuitry configured to generate a learned function using the data outliers.
Dependencies are inferred from the claim text.