US 12,425,432 B2
System and method for network intrusion detection using a neural network implemented by a cloud computing system
Ngoc Anh Tran, Charlotte, NC (US); Manimaran Sundaravel, Chennai (IN); and Maneesh Kumar Sethia, Hyderabad (IN)
Assigned to Bank of America Corporation, Charlotte, NC (US)
Filed by Bank of America Corporation, Charlotte, NC (US)
Filed on Sep. 22, 2023, as Appl. No. 18/473,063.
Prior Publication US 2025/0106232 A1, Mar. 27, 2025
Int. Cl. G06F 15/16 (2006.01); G06F 9/54 (2006.01); G06N 3/08 (2023.01); H04L 9/40 (2022.01); H04L 29/06 (2006.01)
CPC H04L 63/1425 (2013.01) [G06N 3/08 (2013.01); H04L 63/1416 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a plurality of nodes, wherein a first node comprises:
a memory configured to store:
a request rate threshold; and
a block list, wherein the block list comprises a list of malicious parameter values associated with parameters of malicious requests, and wherein a malicious request comprises a request that originates from a malicious user; and
a processor communicatively coupled to the memory, the processor configured to implement a neural network module, wherein the processor is configured to:
intercept a plurality of requests directed to a service provider system from a respective geographical region for a first duration, wherein each request comprises a request for a service from the service provider system;
analyze the plurality of requests to identify authenticated requests from the plurality of requests, wherein the authenticated requests originate from one or more authenticated users;
identify remaining requests as suspicious requests;
analyze each suspicious request to determine respective geolocation information of a respective location from which each suspicious request originates;
group the suspicious requests into a plurality of request groups based on the determined geolocation information, wherein:
a first request group is associated with a first geolocation information and comprises a first plurality of suspicious requests associated with the first geolocation information; and
a second request group is associated with a second geolocation information and comprises a second plurality of suspicious requests associated with the second geolocation information, wherein the second geolocation information is different from the first geolocation information;
determine a first rate of requests for the first request group;
determine a second rate of requests for the second request group; and
in response to determining that the first rate of requests for the first request group is less than or equal to the request rate threshold:
analyze parameters of a first suspicious request of the first request group to determine values of the parameters of the first suspicious request; and
in response to determining that the value of the parameters of the first suspicious request do not match with respective malicious parameter values:
analyze the first suspicious request using the neural network module to identify if the first suspicious request is legitimate or malicious; and
in response to identifying that the first suspicious request is malicious:
 send a first notification that the first suspicious request is identified as malicious;
 add the values of the parameters of the first suspicious request to the block list; and
 synchronize the block list with other nodes of the plurality of nodes.