Gamified XR Phishing Training with Sequential User-Specific Attacks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users in extended reality (XR) environments, such as the metaverse or mixed reality, are susceptible to phishing attacks due to the real-time nature and convincing avatars that deceive them into providing confidential information, with existing training methods being inadequate in preventing such attacks.
Innovation Solution
A gamified training system that uses sequential cross model training and generative models to simulate phishing attacks in real-time XR environments, incorporating actual and artificially generated phishing techniques, and Deepfake technologies to deceive users, while scoring their responses to enhance recognition and prevention skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If phishing attacks are conducted in real-time XR environments with convincing avatars, then the effectiveness of phishing attacks increases, but the time for users to recognize and respond to threats decreases
Solution Approach 1:
The system performs preliminary training actions by exposing users to simulated phishing scenarios in advance before real attacks occur. The gamified training environment pre-acclimates users to various phishing techniques, making them more prepared to recognize and respond to actual threats when they encounter them in real-time XR environments.
Solution Approach 2:
The system implements periodic training sessions with varying phishing scenarios delivered at different time intervals. Users receive repeated exposure to phishing attempts through gamified activities, reinforcing their ability to recognize patterns and respond appropriately under time pressure in real XR environments.
2Ease of operation
If traditional phishing training methods are used, then implementation simplicity is maintained, but training effectiveness in XR environments is insufficient
Solution Approach 1:
The system introduces a gamified training environment as an intermediary between traditional training methods and real XR phishing scenarios. This intermediate layer provides a safe, controlled space where users can practice recognizing phishing attempts without real consequences, bridging the gap between simple instruction and complex real-world application.
Solution Approach 2:
The system replaces traditional mechanical training delivery methods (presentations, documents) with immersive XR-based gamified experiences. By substituting passive information delivery with active, immersive simulation, the system maintains ease of deployment while dramatically improving training effectiveness in realistic XR environments.
3Ease of manufacture
If generic phishing training is provided, then training deployment is simplified, but user-specific vulnerability assessment is limited
Solution Approach 1:
The system implements dynamic training scenarios that automatically adapt to each user's responses and performance. The gamified environment adjusts difficulty levels, scenario types, and timing based on individual user interactions, transforming static generic training into dynamic personalized experiences that evolve with each user's progress and vulnerabilities.
Solution Approach 2:
The system changes multiple parameters including scenario complexity, time constraints, and feedback mechanisms based on individual user performance. By dynamically adjusting these parameters, the system maintains simplified deployment architecture while achieving high adaptability to individual user needs and vulnerability profiles.
Data Source
AI summary
A system, computer program product, and method for training users in phishing prevention may challenge a user with gamified user-specific phishing attacks for a user to encounter in a temporal pattern. Phishing data, including data relating to phishing techniques and actual and non-actual (fake) phishing attacks, may be generated or obtained and input to the training engine. User-related data, including user information, one or more social engineering vectors, or organization information relating to an organization, may be obtained and digitally manipulated. A sequential cross model training engine may process the phishing data and digitally manipulate the user-related data to generate training activities that include test phishing attacks. A gamification engine may generate gamified user-specific testing that includes two or more of the test phishing attacks in a sequence in real time. The test phishing attacks, which may be in a non-question format, may target the user in an XR environment.