Email Deluge Detection Using AI Profile and Content Rules
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Solution Overview
Problem
Enterprise networks face challenges in efficiently detecting and protecting against email deluges, which can overwhelm network resources and lead to poor performance and potential malware spread, resulting in reputational and financial losses.
Innovation Solution
A computing platform trains an AI engine to analyze historical electronic messages, generating profile and content rules to detect email deluges by identifying user profiles and message characteristics, allowing for automated quarantine and resource preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional email filtering methods are used, then network resources are consumed by processing large volumes of emails, but detection accuracy is insufficient and cannot effectively identify email deluges
Solution Approach 1:
The system performs preliminary analysis by extracting features from email headers and content before full processing, using AI models to predict malicious patterns in advance. This allows the system to quickly identify potential deluges and apply more rigorous filtering only when needed, reducing overall network resource consumption while maintaining high detection accuracy.
Solution Approach 2:
The patent replaces traditional mechanical email filtering methods with AI-based machine learning models that can automatically learn and adapt to new email deluge patterns. The AI engine analyzes email features and predicts malicious content without requiring manual rule configuration, significantly improving detection accuracy while reducing the computational overhead of processing every email through complex filtering rules.
2Measurement precision
If all received emails are thoroughly analyzed, then detection accuracy improves, but processing time increases and network performance deteriorates
Solution Approach 1:
The system applies partial analysis to all emails by extracting only critical features from headers and initial content, then uses AI models to assess risk levels. For low-risk emails, processing is terminated early; for high-risk emails, more thorough analysis is performed. This selective approach maintains high detection accuracy while significantly reducing average processing time across the entire email volume.
Solution Approach 2:
The email analysis process is segmented into multiple stages: header analysis, content sampling, AI prediction, and full scanning only for suspected malicious emails. This segmentation allows the system to quickly filter out benign emails using lightweight checks while reserving intensive processing resources only for emails that require deeper inspection, thereby reducing overall processing time without compromising detection accuracy.
3Measurement precision
If manual email filtering rules are created, then detection precision can be improved, but system complexity and maintenance burden increase
Solution Approach 1:
The AI engine automatically learns from email patterns and self-updates detection models without requiring manual rule creation or configuration. The system continuously trains on new email data, automatically adapting to emerging deluge patterns and tactics. This self-service capability maintains high detection precision while eliminating the complexity and maintenance burden associated with manual rule management.
Solution Approach 2:
The system implements feedback loops where detected email deluges and their characteristics are fed back into the AI training process. This continuous feedback mechanism allows the system to automatically refine its detection precision based on real-world performance data, eliminating the need for manual rule adjustments while maintaining or improving detection accuracy over time.
Data Source
AI summary
Arrangements for detecting an email deluge are provided. A computing platform may train an artificial intelligence (AI) engine. The computing platform may receive a group of electronic messages. The computing platform may identify a user profile based on an electronic user identifier associated with the group of electronic messages. The computing platform may determine one or more electronic characteristics based on content associated with the group of electronic messages. The computing platform may generate one or more rules to determine whether a deluge has been detected. Based on determine that a deluge has been detected, the computing platform may quarantine the group of electronic messages and/or perform other actions.