Email Filtering System Using Containerized Intermediary
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Solution Overview
Problem
Current email systems lack the capability to detect and prevent the dissemination of unauthorized email messages, such as those sent by automailers, which can lead to spam, phishing, and other malicious communications.
Innovation Solution
A system that includes a containerized environment with an interface to detect and block unauthorized email messages by checking conditions such as the number of containers instantiated, software downloads, and sender verification against a user directory, using microservices to validate and remediate suspicious email traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If email systems allow free transmission of messages, then email service accessibility and ease of operation are improved, but the system becomes vulnerable to unauthorized email messages, spam, and phishing attacks
Solution Approach 1:
The patent introduces an intermediary component (email filtering system) that sits between the email transmission infrastructure and the user inbox. This intermediary automatically analyzes incoming emails, detects suspicious characteristics (such as automated mailer patterns, phishing indicators), and filters out unauthorized messages before they reach users. This resolves the contradiction by maintaining open email accessibility while blocking harmful content through an intermediate security layer.
Solution Approach 2:
The system performs preliminary security checks and analysis on incoming email messages before they are delivered to user inboxes. By pre-screening emails for unauthorized content, spam indicators, and phishing characteristics, the system prevents harmful messages from entering the email delivery pipeline. This preliminary action ensures that legitimate emails continue to flow freely while malicious content is blocked in advance.
2Object-affected harmful factors
If email systems implement security filtering, then protection against unauthorized messages is improved, but system complexity and difficulty of operation increase
Solution Approach 1:
The email filtering system operates autonomously using automated analysis algorithms, machine learning models, and predefined detection rules. The system self-manages the complex tasks of scanning, analyzing, and filtering emails without requiring manual intervention or complex configuration by users. This self-service approach handles the computational complexity internally while presenting a simple interface to end users, resolving the contradiction between security effectiveness and system simplicity.
Solution Approach 2:
The security filtering function is segmented into separate, specialized components that handle different aspects of email analysis (e.g., spam detection, phishing detection, automated mailer detection). Each segment focuses on specific detection tasks using dedicated algorithms and heuristics. This modular segmentation manages overall system complexity by dividing the filtering function into independent, manageable units that can operate in parallel.
3Productivity
If automated filtering is implemented, then productivity in blocking spam is improved, but the risk of false positives affecting legitimate emails increases
Solution Approach 1:
The email filtering system incorporates feedback mechanisms that continuously learn from filtering outcomes. When users mark filtered emails as legitimate or when the system encounters new email patterns, this information feeds back into the detection algorithms to refine future filtering decisions. The system adjusts its sensitivity and thresholds based on feedback, reducing false positives while maintaining high productivity in blocking actual spam and unauthorized messages.
Solution Approach 2:
The filtering system dynamically adjusts detection parameters and thresholds based on the characteristics of incoming email traffic and observed spam patterns. By changing parameters such as detection sensitivity, confidence thresholds, and analysis depth, the system optimizes the balance between filtering productivity and reliable delivery of legitimate emails. These parameter adjustments allow the system to adapt to different email volumes and threat levels without manual reconfiguration.
Data Source
AI summary
A method, including: receiving an email message, the email message being generated by a computing device; detecting whether a condition associated with the email message is satisfied, the condition including a condition for detecting whether the email message is sent by an automailer that is executing on the computing device; forwarding the email message to an email server, when the condition is satisfied; and discarding the email message, when the condition is not satisfied.