US 12,015,629 B2
Tailored network risk analysis using deep learning modeling
Qihong Shao, Clyde Hill, WA (US); David John Zacks, Vancouver (CA); Yue Liu, Santa Clara, CA (US); and Xinjun Zhang, San Jose, CA (US)
Assigned to CISCO TECHNOLOGY, INC., San Jose, CA (US)
Filed by Cisco Technology, Inc., San Jose, CA (US)
Filed on Sep. 28, 2020, as Appl. No. 17/034,241.
Prior Publication US 2022/0103586 A1, Mar. 31, 2022
Int. Cl. H04L 9/40 (2022.01); G06F 18/211 (2023.01); G06F 40/30 (2020.01); G06N 3/02 (2006.01); H04L 43/065 (2022.01)
CPC H04L 63/1433 (2013.01) [G06F 18/211 (2023.01); G06F 40/30 (2020.01); G06N 3/02 (2013.01); H04L 43/065 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
obtaining a plurality of risk reports relating to a network device in a network, wherein each risk report is associated with a particular dimension of a plurality of dimensions of risk for the network device in the network, wherein the plurality of risk reports are obtained from one or more providers of hardware or software for the network device, and wherein the plurality of dimensions of risk include one or more of: a best practices dimension, a security advisories dimension, and a field notices dimension;
determining a count of the plurality of risk reports for each dimension of the plurality of dimensions of risk; and
applying a regression model 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.