Interactive Email Warning Tags for User-Verified Threat Detection
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Solution Overview
Problem
Current email warning systems fail to detect various reasons why an email is potentially suspicious and/or malicious, do not provide actionable and specific information for users to respond to suspicious and/or malicious emails, and lack flexible reporting tools that can be used across different email service providers and clients.
Innovation Solution
A flexible and automated system that intercepts emails, generates interactive email warning tags (EWTs) with unique links for reporting suspicious content, and allows users to interactively flag and report emails, enabling follow-up enforcement actions, while using machine learning to improve analysis over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated email analysis is used to detect malicious content, then productivity is improved, but measurement precision deteriorates because automated systems cannot detect all suspicious patterns
Solution Approach 1:
The system implements a feedback loop where user reports of suspicious emails are collected and used to retrain the machine learning model. The unique link in each email warning tag enables users to report emails, and this feedback is fed back into the system to improve future detection accuracy, resolving the contradiction between automated efficiency and detection precision.
Solution Approach 2:
The patent introduces an intermediary human user component between the automated analysis system and the final detection outcome. While the automated system provides initial screening, human users provide additional verification through the reporting mechanism, combining the speed of automation with the precision of human judgment.
2Measurement precision
If detailed analysis of all emails is performed, then measurement precision is improved, but use of energy and computational resources worsens
Solution Approach 1:
The system applies partial action by performing detailed analysis only on emails that are flagged as potentially malicious by the initial automated screening. The machine learning model identifies suspicious emails, and only these selected emails undergo more intensive analysis or user review, avoiding the energy cost of analyzing every email in detail while maintaining high precision for problematic messages.
3Reliability
If email warning tags are injected into all emails, then reliability of threat information is improved, but device complexity worsens due to additional processing requirements
Solution Approach 1:
The system applies local quality by injecting email warning tags only into emails that are identified as potentially malicious by the machine learning model, rather than adding tags to all emails. This selective approach maintains reliable threat information delivery for problematic emails while avoiding the unnecessary complexity of processing and tagging every email in the system.
4Measurement precision
If machine learning models are continuously retrained with user feedback, then measurement precision is improved over time, but loss of time for training and processing increases
Solution Approach 1:
The system implements continuous improvement through ongoing collection of user feedback via the unique reporting links. Rather than periodic batch training, the system continuously accumulates labeled data from user reports and retrains the machine learning model, maintaining and improving detection accuracy over time without significant interruptions or time losses.
Data Source
AI summary
Aspects of the disclosure relate to providing a flexible and automated system for automatically detecting when emails include harmful content, flagging the emails, providing interactive reporting functionality, and providing follow-up enforcement actions to protect users. A computing platform may intercept an email in transit to an email server. Subsequently, the computing platform may analyze the email and generate at least one unique link for reporting suspicious content associated with the email. Next, the computing platform may generate an email warning tag comprising text information and the at least one unique link for reporting the suspicious content associated with the email. Then, the computing platform may inject the email warning tag into the email to produce a modified email comprising content from the email and the email warning tag, and may send the modified email to the email server.