Email Sender Authentication via Probabilistic Pattern Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing email protocols do not effectively validate the identity of email senders, leading to phishing attacks where malicious actors impersonate legitimate senders, and existing solutions are not robust enough to prevent sophisticated phishing attempts, especially since they are not integrated into email servers and clients.
Innovation Solution
A computer-implemented method for analyzing electronic messages that involves receiving a message, identifying actual sender message characteristics, probabilistically comparing these characteristics to determine the similarity with historical patterns, and influencing the probability that the indicated sender is the actual sender, using a classification engine trained on historical messages and threat intelligence data to alert recipient organizations about potential phishing threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing email protocols and simple detection methods (IP address checking, URL checking) are used, then the system is easy to operate and has low device complexity, but the reliability and measurement precision are insufficient to detect sophisticated phishing attacks
Solution Approach 1:
The patent segments the sender identity verification into multiple independent characteristics (IP address, URL, message headers, content patterns, metadata) that are analyzed separately and then combined. This allows the system to maintain simplicity in individual checks while achieving high reliability through comprehensive multi-factor analysis.
Solution Approach 2:
The detection system is designed to perform multiple functions: checking IP addresses, analyzing URLs, examining message headers, evaluating content patterns, and assessing metadata. This multi-functional approach enables a single system to address various phishing techniques without requiring separate specialized tools for each detection method.
2Measurement precision
If comprehensive message characteristic analysis is performed, then the measurement precision of sender identification improves, but the loss of time and processing overhead increase
Solution Approach 1:
The system performs preliminary analysis of message characteristics as messages are received, building profiles of legitimate senders in advance. By pre-establishing baseline characteristics and patterns for known legitimate senders, the system can quickly compare incoming messages against these pre-computed profiles, reducing real-time processing time while maintaining high identification precision.
Solution Approach 2:
The patent applies different levels of analysis depth to different message characteristics based on their reliability and processing cost. Critical characteristics like IP address and message headers receive immediate thorough analysis, while less critical features are analyzed with lower computational overhead. This localized quality adjustment optimizes the balance between precision and processing time.
Data Source
AI summary
Systems and methods for analyzing electronic messages are disclosed. In some embodiments, the method comprises receiving a new received message from an indicated sender, the new received message having a first message characteristic of the indicated sender and a second message characteristic, identifying an actual sender message characteristic pattern of an actual sender using the first message characteristic, probabilistically comparing the second message characteristic to the actual sender message characteristic pattern, determining a degree of similarity of the second message characteristic to the actual sender message characteristic pattern, and influencing a probability that the indicated sender is the actual sender based upon the degree of similarity. There may be multiple message characteristics and patterns. In some embodiments, the methods may utilize pattern matching techniques, recipient background information, quality measures, threat intelligence data or URL information to help determine whether the new received message is from the actual sender.