NETWORK DATA PROCESSING DRIVER FOR A COGNITIVE ARTIFICAL INTELLIGENCE SYSTEM

• US 20170295070A1
• Filed: 04/06/2017
• Published: 10/12/2017
• Est. Priority Date: 04/06/2016
• Status: Active Grant

• Current Assignee: Intellective Ai, Inc.
• Original Assignee: Ming-Jung Seow, Tao Yang
• Inventors: YANG, Tao, SEOW, Ming-Jung
• Granted Patent: US 10,454,777 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 41/12 Discovery or management of network topologies
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

Techniques are disclosed for processing data collected from network components for analysis by a machine learning engine of a Cognitive AI System. A network data processing driver receives a stream of data from a data collector which obtains data from one or more network data sources. The driver normalizes the stream of data to one or more feature values each corresponding to the network data sources and generates a sample vector from the feature values. The sample vector is formatted to be analyzed by the machine learning engine.

Claims

1. A computer-implemented method to generate a logical network topology for a network, the method comprising:

receiving a stream of data from a data collector within the network, the stream of data obtained from a data source within the network;

normalizing the stream of data to extract a feature value, the feature value associated with the data source;

generating statistics by tracking patterns associated with the feature value, the feature value changing over time with new stream of data received from the data collector;

generating the logical network topology based on the statistics;

generating a context-aware description of network activity based at least in part on a mapping of the feature value in the logical network topology ; and

sending a signal representing the context-aware description of the network activity , the signal representing an anomaly in the network activity.