Adaptive Phishing Training System Using AI Model Trainer
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Solution Overview
Problem
Phishing attacks are becoming increasingly sophisticated, and existing security awareness systems struggle to effectively train users to detect highly individualized and real-time threats, as they lack the ability to create a simulated phishing environment that mimics real-world attacks.
Innovation Solution
A security awareness system utilizing artificial intelligence and machine learning to adaptively design and execute simulated phishing campaigns by varying message parameters such as quantity, frequency, and content, and learning from user responses to improve the effectiveness of training, with the ability to create personalized phishing scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional simulated phishing attacks are used with fixed content and timing, then the attack structure is simple and easy to implement, but the training effectiveness is reduced because it does not match sophisticated real-world phishing attacks
Solution Approach 1:
The system dynamically adapts phishing message parameters including content, timing, frequency, and targeting based on user responses and learned behaviors. The simulated phishing environment transitions from static to dynamic, allowing the system to evolve its attack patterns to better resemble sophisticated real-world phishing attempts while providing more effective training
Solution Approach 2:
The system changes multiple parameters of phishing messages including content variations, sending timing, frequency, and recipient targeting. By systematically varying these parameters based on user responses, the system creates more realistic training scenarios that adapt to individual user behaviors and improve training effectiveness
2Reliability
If personalized and adaptive simulated phishing campaigns are implemented, then training effectiveness improves by matching real-world attack patterns, but the complexity of designing and executing these campaigns increases
Solution Approach 1:
The system automatically learns from user responses and independently generates adapted phishing campaigns without requiring manual intervention. The machine learning algorithms autonomously analyze user behaviors, update attack patterns, and execute personalized simulated phishing campaigns, reducing the need for human operators while improving training effectiveness
Solution Approach 2:
The system implements continuous feedback loops where user responses to phishing attempts are automatically analyzed and used to refine future campaign parameters. This feedback mechanism enables the system to learn from each interaction and progressively improve the personalization and realism of simulated phishing attacks
3Adaptability or versatility
If multiple varied simulated phishing messages are sent to users, then the ability to train users improves by covering different attack scenarios, but the quantity of messages and system resources required increases
Solution Approach 1:
The system tailors phishing messages to individual user characteristics, behaviors, and risk profiles rather than sending generic messages to all users. By customizing content, timing, and targeting for each user based on their specific patterns, the system achieves comprehensive training coverage with a more efficient use of message quantity
Data Source
AI summary
Disclosed embodiments describe an exporter that creates files in a format suitable as input for training models, from records selected and extracted from a database which stores results from simulated phishing campaigns. In some embodiments, a model trainer receives the files from the exporter and uses the files as inputs to train a neural network in order to establish a model. The model is the stored to be used by a campaign controller for communicating simulated phishing communications to devices of users, as part of a simulated phishing campaign.