US 12,278,830 B2
Systems and methods using network artificial intelligence to manage control plane security in real-time
Gopi Sirineni, San Jose, CA (US); Raghu Kondapalli, San Jose, CA (US); and Alexander Bachmutsky, Sunnyvale, CA (US)
Assigned to Axiado Corporation, San Jose, CA (US)
Filed by Axiado Corporation, San Jose, CA (US)
Filed on Aug. 29, 2022, as Appl. No. 17/897,676.
Claims priority of provisional application 63/238,933, filed on Aug. 31, 2021.
Prior Publication US 2023/0060207 A1, Mar. 2, 2023
Int. Cl. H04L 9/40 (2022.01); G06N 3/08 (2023.01); H04L 41/16 (2022.01); H04L 67/60 (2022.01); H04L 69/22 (2022.01); G06F 21/55 (2013.01); G06F 21/56 (2013.01)
CPC H04L 63/1425 (2013.01) [G06N 3/08 (2013.01); H04L 41/16 (2013.01); H04L 63/0263 (2013.01); H04L 63/1441 (2013.01); H04L 67/60 (2022.05); H04L 69/22 (2013.01); G06F 21/554 (2013.01); G06F 21/56 (2013.01); G06F 21/562 (2013.01); H04L 63/1416 (2013.01)] 31 Claims
OG exemplary drawing
 
1. A computer-implemented method of detecting malicious activity in a network comprising:
a) feeding network packets from the network into a header crypto engine for processing;
b) sending the network packets from the header crypto engine to a work scheduler;
c) divaricating the network packets using the work scheduler based on flow data and header data of the network packets to at least one of a static rule checker in a firewall, a wild card rule checker in the firewall, a denial-of-service engine in the firewall, and a neural network processor;
d) generating output data comprising:
i) a first output data from the firewall according to rules of the firewall; and
ii) a second output data from the neural network processor based on behavioral analysis performed by the neural network processor, wherein the second output data is used to update the rules in the firewall; and
e) aggregating the output data from the firewall and the neural network processor to detect malicious activity in the network.