US 12,452,269 B2
Machine learning (ML) based systems for air gapping network ports
Ioannis (Giannis) Patronas, Piraeus (GR); Tamar Viclizki Cohen, Herzliya (IL); Vadim Gechman, Kibbutz Hulda (IL); Dimitrios Syrivelis, Volos (GR); Paraskevas Bakopoulos, Ilion (GR); Nikolaos Argyris, Zografou (GR); and Elad Mentovich, Tel Aviv (IL)
Assigned to Mellanox Technologies, Ltd., Yokneam (IL)
Filed by Mellanox Technologies, Ltd., Yokneam (IL)
Filed on Sep. 29, 2022, as Appl. No. 17/956,208.
Claims priority of application No. 2022/0100760 (GR), filed on Sep. 19, 2022.
Prior Publication US 2024/0098104 A1, Mar. 21, 2024
Int. Cl. H04L 9/40 (2022.01); H04L 41/16 (2022.01); H04L 47/17 (2022.01)
CPC H04L 63/1425 (2013.01) [H04L 41/16 (2013.01); H04L 47/17 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A machine learning (ML) based system for air gapping network ports, the system comprising:
a non-transitory storage device; and
a processor coupled to the non-transitory storage device, wherein the processor is to:
monitor data traffic across network ports in a network environment;
determine a first data traffic pattern from the data traffic;
determine, via a ML subsystem, that the first data traffic pattern is indicative of a security threat to a first network port; and
in response to determining that the first data traffic pattern is indicative of the security threat to the first network port, (i) isolate the first network port from the network ports, and (ii) trigger an intermediate network switch to reroute the data traffic from the first network port to a redundant network port,
wherein the first network port, the redundant network port, and the intermediate network switch are associated with a first network port cluster.