Adaptive URL Awareness Training With In-Workflow Feedback
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Solution Overview
Problem
Conventional phishing URL detection systems lack effectiveness in identifying phishing URLs with high accuracy, and existing cybersecurity training methods require employees to take time out from their work schedule for static, generic training that does not adapt to individual performance or provide continuous learning opportunities.
Innovation Solution
A method and system for adaptive real-time URL awareness training that identifies employees' browsing behavior, provides in-situ training through a browser extension, and dynamically adjusts training content based on individual performance, using AI-generated feedback and spaced repetition to enhance URL component recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional phishing URL detection systems are used, then multiple layers of defense are provided, but the accuracy in identifying phishing URLs is insufficient
Solution Approach 1:
The system implements continuous feedback loops where user interactions with URLs are monitored, training recommendations are generated, user responses are collected, and the model is retrained iteratively. This feedback mechanism enables the system to learn from actual user behavior and improve phishing detection accuracy over time without requiring complex manual configuration.
Solution Approach 2:
The system performs self-training by automatically collecting browsing data, generating training datasets, and retraining the phishing detection model without requiring external intervention. The system serves itself by using its own operational data to improve its detection capabilities, reducing the need for complex external training infrastructure.
2Adaptability or versatility
If static generic training content is provided to employees, then training coverage is achieved, but the training does not adapt to individual performance or provide continuous learning opportunities
Solution Approach 1:
The training system dynamically adapts content based on individual user performance metrics, browsing behavior patterns, and knowledge gaps. Training recommendations are continuously updated in real-time as user interactions are monitored, ensuring each employee receives personalized training rather than static generic content. This dynamic adaptation eliminates the need for employees to take time from their schedules for standardized training sessions.
Solution Approach 2:
The system provides continuous training opportunities by monitoring user browsing behavior in real-time and delivering training recommendations contextually during work hours. Instead of requiring employees to stop work for dedicated training sessions, the system continuously provides learning opportunities integrated into the natural workflow, maintaining productivity while delivering training.
3Reliability
If employees are required to log into isolated systems for cybersecurity training, then dedicated training time is allocated, but productivity is reduced due to time taken from work schedule
Solution Approach 1:
The system merges training delivery with the employees' existing browsing environment by injecting training content directly into the browser extension. Instead of requiring employees to log into separate isolated training systems, the training is combined with their regular work browser, allowing them to complete training tasks while remaining in their productive work context, thus maintaining both training completion and productivity.
Solution Approach 2:
The browser extension acts as an intermediary that delivers training content and collects user responses without requiring employees to leave their work environment. The extension mediates between the training system and the employee's browsing activity, enabling training to occur seamlessly within the existing workflow rather than requiring separate isolated training sessions.
4Measurement precision
If conventional training methods are used, then generic training content is provided to groups, but individual performance differences and specific knowledge gaps are not addressed
Solution Approach 1:
The system applies local quality by providing personalized training recommendations tailored to each employee's specific knowledge gaps, browsing behavior patterns, and performance metrics. Instead of uniform generic training for all employees, the system analyzes individual data and delivers targeted training content specific to each user's needs, with complexity automatically adjusted based on individual performance levels.
Data Source
AI summary
Organizations across the world faces major losses due to cyber-attacks. Hence training users regarding URL can reduce the chances of cyber-attacks. The training content offered by the existing training platform is generic and static in nature. Hence there is a challenge in providing dynamic training content without exploiting working hours of users/employees. The present disclosure provides real time cybersecurity training for users which provides continuous feedback and dynamic content to train the users in URL components. This training allows employees to learn and apply their skills in their actual work environment, making it more practical and relevant. The present disclosure computes priority of training content to be displayed based on user performance and weight associated with URL components dynamically.