Phishing Message Template Matching for Individual Susceptibility Testing
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Solution Overview
Problem
Conventional social engineering awareness training methods are ineffective due to their generalized approach, failing to account for individual susceptibilities to phishing attacks, leading to inefficiencies and potential security breaches.
Innovation Solution
A system utilizing generative and predictive machine learning models to generate customized electronic message templates tailored to individual susceptibilities, enabling targeted phishing simulations for improved training and vulnerability testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional generalized phishing emails are distributed to all individuals, then the training program can be implemented simply, but the effectiveness of testing individual susceptibilities is reduced
Solution Approach 1:
The system segments the population into individual users and segments the phishing messages into multiple templates with different characteristics. Instead of using a single generic phishing email for all users, the system creates distinct message variants (e.g., urgent tone, friendly tone, professional tone) and matches them to individual users based on their susceptibility profiles, thereby improving effectiveness while managing complexity through structured segmentation.
Solution Approach 2:
The system applies local quality by customizing the phishing message characteristics to match individual user profiles and susceptibility levels. Each user receives phishing messages tailored to their specific characteristics (e.g., job role, communication style, known vulnerabilities) rather than a uniform approach, thereby improving the relevance and effectiveness of the training while addressing individual-specific needs.
2Reliability
If a large number of electronic messages are created and distributed to all individuals, then the coverage of testing all individuals is improved, but the time consumption and network resource waste increase
Solution Approach 1:
The system applies partial action by selecting and sending only the most relevant phishing message templates to each individual user based on their susceptibility metrics, rather than distributing all possible message variants to everyone. This selective approach ensures comprehensive coverage of individual testing while reducing the total number of messages sent, thereby decreasing time consumption and network resource usage.
Solution Approach 2:
The system changes the parameters of message selection and distribution based on individual user characteristics and susceptibility profiles. Instead of using fixed parameters for all users, the system dynamically adjusts message characteristics (tone, content, timing) based on individual data, allowing efficient targeted delivery that reduces overall time consumption while maintaining comprehensive testing coverage.
3Adaptability or versatility
If conventional generalized phishing emails are used, then the system implementation is simple, but the ability to probe specific individual weaknesses is reduced
Solution Approach 1:
The system performs preliminary actions by pre-classifying users into susceptibility categories and pre-preparing multiple phishing message templates with different characteristics before the actual training campaign. This advance preparation allows the system to quickly match appropriate messages to individuals without complex real-time generation, thereby improving the ability to probe individual weaknesses while maintaining ease of implementation through pre-computed classifications and templates.
Solution Approach 2:
The system uses copying by creating multiple copies of phishing message templates with varying characteristics (tone, content, formatting) that can be selectively deployed to different user segments. Instead of manually creating custom messages for each user, the system copies and adapts existing template patterns, which simplifies the implementation process while enabling targeted probing of individual weaknesses through strategic template selection.
Data Source
AI summary
A system is configured to generate a plurality of electronic message templates by applying a generative machine learning model to electronic message feature data. The system generates a plurality of susceptibility metrics using a predictive ML model, wherein each susceptibility metric indicates a predicted probability of a respective individual, of a plurality of individuals, interacting with an electronic message generated using a respective electronic message template of the plurality of electronic message templates. For each individual, the system may select a particular electronic message template based at least upon the susceptibility metric associated with the individual and the particular electronic message template, generate a respective electronic message based upon the particular electronic message template, and cause the respective electronic message to be provided to a user device of the individual.