AI Phishing Lure Generation for Personalized Training at Scale
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Solution Overview
Problem
Existing cybersecurity training tools face challenges in efficiently generating personalized phishing lures at scale, distinguishing between synthetic and manually generated phishing messages, and detecting live phishing threats effectively.
Innovation Solution
A computing platform uses artificial intelligence, specifically generative pre-trained transformers (GPT2/GPT3), to generate personalized phishing lures based on user-specific and regional data, and updates the model with user feedback, while an inbound message filtering system distinguishes between synthetic and manually generated phishing messages using machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual development of phishing training messages is used, then customization and effectiveness are improved, but resource consumption and deployment scalability worsen
Solution Approach 1:
The system uses AI to automatically generate multiple phishing message variations by copying and adapting patterns from real phishing messages, eliminating the need for manual creation of each training message while maintaining customization and effectiveness
Solution Approach 2:
The system changes parameters such as sender name, subject line, message body, and recipient information dynamically to generate personalized phishing training messages at scale, allowing extensive customization without manual intervention for each message
2Productivity
If generic phishing training messages are used, then deployment scalability is improved, but effectiveness and user engagement worsen
Solution Approach 1:
The system applies local quality by personalizing specific elements of phishing messages (sender, subject, body content) based on recipient characteristics and role, making each message locally adapted to the target user while maintaining automated scalable deployment
3Reliability
If inbound messaging systems deflect all suspicious messages, then security is improved, but legitimate training messages are incorrectly blocked
Solution Approach 1:
The system introduces an intermediary classification layer that distinguishes between malicious phishing messages and benign training messages, allowing the inbound messaging system to reliably detect and block threats while permitting authorized training messages to pass through
4Productivity
If AI-generated synthetic phishing lures are used, then scalability and efficiency are improved, but difficulty in distinguishing from real phishing messages worsens
Solution Approach 1:
The system converts the potential harm of AI-generated synthetic lures being indistinguishable from real phishing messages into a benefit by using them to train detection systems, improving the ability to identify both synthetic and manual phishing attempts
Data Source
AI summary
Aspects of the disclosure relate to automated simulated phishing lure generation for cybersecurity training. The computing platform may receive personalization data. The computing platform may generate, using a phishing lure generation model, one or more simulated synthetic phishing lures based on the personalization data. The computing platform may send the one or more simulated synthetic phishing lures to one or more user devices and one or more commands directing the one or more user devices to display the one or more simulated synthetic phishing lures, which may cause the one or more user devices to display the one or more simulated synthetic phishing lures. The computing platform may receive, from the one or more user devices, feedback data corresponding to user interactions with the simulated one or more synthetic phishing lures. The computing platform may update, using the feedback data, the phishing lure generation model.