Invention Grant
- Patent Title: Systems and methods for cyber-attack detection at sample speed
-
Application No.: US15484282Application Date: 2017-04-11
-
Publication No.: US10594712B2Publication Date: 2020-03-17
- Inventor: Lalit Keshav Mestha , Justin Varkey John , Weizhong Yan , David Joseph Hartman
- Applicant: General Electric Company
- Applicant Address: US NY Schenectady
- Assignee: General Electric Company
- Current Assignee: General Electric Company
- Current Assignee Address: US NY Schenectady
- Agency: Buckley, Maschoff & Talwalkar LLC
- Main IPC: G06F21/00
- IPC: G06F21/00 ; H04L29/06 ; G06N20/00 ; G06N20/10 ; G06N3/04 ; G06N3/08 ; G06N7/00

Abstract:
A threat detection model creation computer receives normal monitoring node values and abnormal monitoring node values. At least some received monitoring node values may be processed with a deep learning model to determine parameters of the deep learning model (e.g., a weight matrix and affine terms). The parameters of the deep learning model and received monitoring node values may then be used to compute feature vectors. The feature vectors may be spatial along a plurality of monitoring nodes. At least one decision boundary for a threat detection model may be automatically calculated based on the computed feature vectors, and the system may output the decision boundary separating a normal state from an abnormal state for that monitoring node. The decision boundary may also be obtained by combining feature vectors from multiple nodes. The decision boundary may then be used to detect normal and abnormal operation of an industrial asset.
Public/Granted literature
- US20180159879A1 SYSTEMS AND METHODS FOR CYBER-ATTACK DETECTION AT SAMPLE SPEED Public/Granted day:2018-06-07
Information query