Invention Application
- Patent Title: TAILORED NETWORK RISK ANALYSIS USING DEEP LEARNING MODELING
-
Application No.: US17034241Application Date: 2020-09-28
-
Publication No.: US20220103586A1Publication Date: 2022-03-31
- Inventor: Qihong Shao , David John Zacks , Yue Liu , Xinjun Zhang
- Applicant: Cisco Technology, Inc.
- Applicant Address: US CA San Jose
- Assignee: Cisco Technology, Inc.
- Current Assignee: Cisco Technology, Inc.
- Current Assignee Address: US CA San Jose
- Main IPC: H04L29/06
- IPC: H04L29/06 ; H04L12/26 ; G06N3/02 ; G06F40/30 ; G06K9/62

Abstract:
A method, computer system, and computer program product are provided for network risk analysis. A plurality of risk reports relating to a network device in a network are obtained, wherein each risk report is associated with a particular dimension of a plurality of dimensions of risk for the network device in the network. A count of the plurality of risk reports is determined for each dimension of the plurality of dimensions of risk. A regression model is applied to determine a risk value for the network device in the network based on the count of the plurality of risk reports for each dimension and based a role of the network device in the network.
Public/Granted literature
- US12015629B2 Tailored network risk analysis using deep learning modeling Public/Granted day:2024-06-18
Information query