Phishing Training Tool for Adaptive User Susceptibility Management
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Solution Overview
Problem
Phishing emails pose a significant risk to computing systems and networks as they can deceive a large number of recipients, making it difficult to prevent security threats, and existing methods are inefficient in training users to identify and respond to phishing attempts effectively.
Innovation Solution
A system that communicates different types of phishing emails to users, determines response rates, and adjusts the distribution of subsequent emails to achieve an aggregate response rate closer to a target, thereby reducing the effectiveness of phishing attempts by tailoring training to individual user susceptibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If phishing emails are sent to a large number of users, then the training coverage is improved, but the risk of actual phishing attacks increases
Solution Approach 1:
The system segments the user population into different groups based on their susceptibility to phishing emails. By dividing users into segments with different risk profiles, the system can apply targeted training strategies to each segment, reducing the need to send phishing emails to all users while still achieving comprehensive training coverage.
Solution Approach 2:
The system dynamically changes parameters such as email composition, sending timing, and target user selection based on observed response rates and susceptibility metrics. This allows the system to optimize training effectiveness while minimizing the spread of phishing emails by adjusting these parameters in real-time.
2Measurement precision
If phishing emails are composed to be more deceptive, then the training effectiveness is improved, but the ethical concerns and potential harm increase
Solution Approach 1:
The system uses feedback from user responses to phishing emails to continuously improve training effectiveness. By analyzing response rates and user behavior, the system can adjust future email compositions to be more effective without necessarily increasing deception, as the feedback loop allows for optimization based on actual user susceptibility patterns.
Solution Approach 2:
The system applies partial deception by sending phishing emails only to users who are identified as susceptible based on preliminary assessments. This partial action approach ensures that training is applied where needed without exposing all users to potentially harmful deceptive content, thus reducing overall ethical harm while maintaining training effectiveness for the target population.
3Productivity
If the system sends more phishing emails to achieve target response rates, then the training coverage is improved, but the system complexity and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-assessing user susceptibility to phishing emails before sending the actual training emails. This preliminary assessment allows the system to identify which users need training and send phishing emails only to them, reducing the overall number of emails needed to achieve target response rates and simplifying the system's operational complexity.
Solution Approach 2:
The system uses automated algorithms and machine learning models to self-adjust email composition and distribution strategies based on observed data. This self-service capability reduces the need for manual intervention and complex system management, allowing the system to maintain high training coverage while managing complexity through automation.
Data Source
AI summary
According to one embodiment, an apparatus is configured to communicate a first plurality of phishing emails to a first plurality of users, each phishing email of the first plurality of phishing emails is of a first type or a second type. The apparatus is configured to determine a first response rate of the first plurality of users to phishing emails of the first type and to determine a second response rate of the first plurality of users to phishing emails of the second type. The apparatus is configured to determine a second plurality of phishing emails comprising phishing emails of the first type and the second type, wherein an aggregate response rate of a second plurality of users to the second plurality of phishing emails is predicted to be closer to a target response rate than one or more of the first response rate and the second response rate.