Scott D Sims

US Patent:

20220286476, Sep 8, 2022

Filed:

Mar 3, 2021

Appl. No.:

17/191377

Inventors:

- Charlotte NC, US
Jeffrey Brian Bashore - Saint Augustine FL, US
Joel Filliben - Newark DE, US
Andrew DongHo Kim - Glendale AZ, US
Akhilendra Reddy Kotha - Downingtown PA, US
Pavan Kumar Reddy Kotlo - Middletown DE, US
Dharmender Kumar Satija - Rye Brook NY, US
Michael Shih - Cherry Hill NJ, US
Scott Anderson Sims - Tega Cay SC, US
Craig D. Widmann - Chandler AZ, US

Assignee:

BANK OF AMERICA CORPORATION - Charlotte NC

International Classification:

H04L 29/06

Abstract:

Embodiments of the invention are directed to a system, method, or computer program product for cross-channel network security with tiered adaptive mitigation operations. In this regard, the invention is structured for dynamic detection of security events associated with network devices and resources, and triggering real-time mitigation operations across a plurality of resource channels. The invention provides a novel method for employing activity data to construct and implement mitigation actions for de-escalating authorization tiers that are adapted to the specific attributes of the activity data, in order to prevent security exposure associated with the activity. Another aspect of the invention is directed to determining whether to continue the tiered adaptive mitigation actions and/or trigger a security proceed signal.

US Patent:

20190378010, Dec 12, 2019

Filed:

Jun 12, 2018

Appl. No.:

16/006559

Inventors:

- Charlotte NC, US
Lorraine C. Edkin - Jacksonville FL, US
Scott A. Sims - Tega Cay SC, US
Joel Filliben - Newark DE, US
Margaret A. Payne - Elkton MD, US
Craig Douglas Widmann - Chandler AZ, US
Eren Kursun - New York NY, US

International Classification:

G06N 3/08
G06F 17/30
G06N 3/04

Abstract:

Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.