AI Phishing Detection Training Using Synthetic Spear Phishing Data
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Solution Overview
Problem
Existing cyber threat detection systems struggle to accurately identify spear phishing and whale phishing attacks tailored to specific individuals due to a lack of targeted training data and the increasing sophistication of these attacks, which are often crafted with generative AI to appear legitimate.
Innovation Solution
A system generates synthetic training data using generative AI to create realistic spear phishing emails for different categories of recipients, employing multiple content modalities (text, images, attachments) and filters to refine detection models, which are then fine-tuned based on user feedback to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic phishing detection approaches are used, then detection speed is maintained, but detection accuracy for targeted phishing attacks deteriorates
Solution Approach 1:
The system segments phishing detection into multiple specialized models: a general phishing detector and category-specific detectors (e.g., spear phishing, whale phishing, CEO fraud). Each model is trained on targeted data for its specific category, enabling accurate detection of targeted attacks while maintaining overall system efficiency through modular architecture.
Solution Approach 2:
The system applies local quality by training each detection model with specialized data tailored to its specific phishing category. For example, the CEO fraud detector is trained specifically on CEO-targeted phishing examples, giving it localized expertise that general detectors lack, thereby improving detection accuracy for specific attack types.
2Reliability
If generative AI is used to create realistic phishing messages, then phishing effectiveness increases, but detectability should improve with proper training data
Solution Approach 1:
The system performs preliminary action by proactively generating synthetic phishing training data using generative AI before actual phishing attacks occur. This synthetic data is used to pre-train detection models, ensuring they are prepared to recognize sophisticated AI-generated phishing messages when they appear in production.
Solution Approach 2:
The system converts the harmful capability of generative AI (creating realistic phishing messages) into a benefit by using the same technology to generate synthetic training data. This transforms the threat into a solution, enabling detectors to learn from realistic examples without exposing users to actual phishing risks.
3Measurement precision
If category-specific detection models are trained, then detection precision for targeted attacks improves, but training data requirements increase
Solution Approach 1:
The system uses copying by generating synthetic training data through generative AI that replicates the characteristics of real phishing attacks. Instead of requiring large volumes of actual phishing emails, the system creates copies or simulations of phishing messages that preserve the essential patterns and features needed for effective detection training.
Solution Approach 2:
The system applies parameter changes by transforming the state of training data from scarce real phishing examples to abundant synthetic variations. By adjusting parameters such as message content, sender information, and recipient contexts through generative AI, the system creates diverse training datasets that enable robust category-specific model training without requiring proportional increases in real attack data.
Data Source
AI summary
Approaches in accordance with various illustrative embodiments provide for the generation of synthetic communications for use in training and fine-tuning threat detection models for various categories of recipients. In at least one embodiment, guidelines can be determined for a category of recipient that can be used to generate multiple types of content using generative artificial intelligence (AI), as may include text, image, and file content. A training communication can be generated using these types of content, such as to generate an email message that corresponds to a potential spear phishing attack. The generated messages can be checked for quality, and any messages that are caught by existing filters can be deleted or regenerated so that only high quality examples of spear phishing are provided as output. These training communications can be used to train a spear phishing detector for a specific category of recipient, in order to accurately flag and prevent access to actual spear phishing communications.