Deep learning pipeline for proactive identification and mitigation of computing system attacks from social engineering

A deep learning pipeline analyzes communication patterns to proactively identify and mitigate social engineering attacks by clustering accounts based on shared features, improving security and reducing fraud risk.

WO2026122365A1 Publication Date: 2026-06-11PAYPAL INC
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Patent Information

Application Number
PCT/US2025/057121
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-12-02
Filing Date
2025-11-25
Publication Date
2026-06-11

AI Technical Summary

Technical Problem

Existing systems struggle to accurately and promptly identify social engineering attacks, which are increasingly sophisticated due to the use of advanced AI technologies like ChatGPT, leading to potential fraud and loss, as conventional risk detection methods fail to distinguish between genuine and fraudulent interactions.

Method used

A deep learning pipeline that utilizes neural networks and graph computation to analyze communication patterns, generate relationship graphs, and cluster accounts based on shared features, enabling proactive identification and mitigation of social engineering attacks.

Benefits of technology

The deep learning pipeline enhances security by precisely tagging fraudulent accounts, providing real-time tracking and preemptive fraud detection, reducing the risk of unauthorized data exposure and financial loss.

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Abstract

Computer security improvements relating to detection of social engineering attacks using a deep learning pipeline for account and communication correlations are disclosed. A service provider may utilize a framework having computing operations for detecting social engineering attacks, fraud, and other malicious or suspicious activities by malicious bots and other fraudsters. In this regard, the service provider may extract pattern data from communications using one or more AI models, which may include semantic information. Account assets for different accounts may be correlated using a relationship graph that links the assets based on the communication patterns shared between the assets. The relationship graph may then be used by additional AI models that classify if the communications are associated with social engineering attacks by classifying the communications using an attack classifier. The classification may be based on inferencing by the AI models from training on past social engineering attacks.
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