Spoofed Domain Identification and User Training System
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Solution Overview
Problem
Domain spoofing poses a significant risk to organizations as users may inadvertently access spoofed domains, leading to information collection, malware injection, and damage to brand reputation, with existing technologies failing to effectively identify and mitigate these risks.
Innovation Solution
A security awareness system that identifies potential harmful domains by analyzing associated, spoofed, and look-alike domains, determines their registration status, and generates electronic training campaigns to educate users in distinguishing between legitimate and spoofed domains, incorporating risk assessments and user-specific training based on their role and history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users access domains without verification, then ease of operation is improved, but security risk increases due to spoofed domains
Solution Approach 1:
The system performs preliminary identification of spoofed domains by analyzing domain similarity, registration status, and web server activity before users access domains. Risk indicators are calculated in advance and communicated to users, allowing them to make informed decisions without compromising ease of access to legitimate domains.
2Measurement precision
If the system tracks all third-party registered spoofed domains, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system applies different tracking approaches based on domain characteristics. It specifically tracks privately registered spoofed domains with active web servers as these pose the highest risk, while using less resource-intensive methods for other domains. This selective approach maintains measurement precision for high-risk domains while reducing overall system complexity.
3Reliability
If the system provides comprehensive training to all users, then reliability is improved, but loss of time increases due to training requirements
Solution Approach 1:
The system provides training selectively based on user risk indicators rather than uniformly to all users. Users with higher risk profiles or those who interact with domains showing risk indicators receive targeted training, while low-risk users receive minimal or no training. This approach maintains reliability by training those who need it most while reducing overall time loss.
4Difficulty of detecting and measuring
If the system monitors all domain variations, then detection capability is improved, but loss of information increases due to privacy registration
Solution Approach 1:
The system converts the obstacle of privacy registration into a beneficial detection mechanism. Rather than being blocked by hidden ownership information, the system uses the presence of privacy registration itself as a risk indicator, combined with analysis of domain similarity, active web servers, and other observable characteristics to identify spoofed domains effectively.
Data Source
AI summary
Systems and methods are disclosed that minimize ongoing risk to an organization from user behaviors which magnify the severity of a spoofed domain. Systems and method are provided which enable an entity and users of an entity to identify potential harmful domains, combining search, discovery, reporting, the generation of risk indicators, end-user risk assessments, and training into a security awareness system.