Bank of America Corporation

Joel Filliben

Craig Douglas Widmann

Scott Anderson Sims

Eren Kursun

Dharmender Kumar Satija

Andrew DongHo Kim

Michael Joseph Carroll

Jeffrey Brian Bashore

Ronnie Joe Morris, Jr

Pavan Kumar Reddy Kotlo

Margaret A Payne

Lorraine C Edkin

Dana M Pusey-Conlin

Ronnie J Morris

Michael Shih

Akhilendra Reddy Kotha

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.

Cross-channel network security system with tiered adaptive mitigation operations

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.

Unsupervised machine learning system to automate functions on a graph structure

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Newark, DE, United States of America

Joel Filliben