Message Impersonation Detection Using User-Specific Writing Models
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Solution Overview
Problem
Existing technologies struggle to automatically identify messages that include impersonated content, particularly when a user account is compromised, and efficiently balance security with computing resource consumption.
Innovation Solution
A computing platform generates user identification models based on email data to analyze writing styles, applies impersonation algorithms to feature vectors, and modifies message delivery based on confidence scores and deviation thresholds to detect and respond to impersonation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated impersonation detection processes are applied to large numbers of messages, then security detection capability is improved, but computing resource consumption (processing power and network bandwidth) increases
Solution Approach 1:
The system segments the impersonation detection process into multiple stages: initial filtering using lightweight rules, followed by progressive application of more complex analysis techniques only to suspicious messages. This hierarchical segmentation allows comprehensive security checking while minimizing overall computing resource consumption by avoiding full analysis on all messages.
Solution Approach 2:
The system applies partial analysis to all messages (basic header and sender verification) and reserves excessive/detailed analysis (full content linguistic analysis, behavioral pattern matching) only for messages that trigger suspicion thresholds. This selective application of analysis depth optimizes the balance between detection capability and resource usage.
2Measurement precision
If comprehensive message analysis techniques are used to detect impersonation, then detection accuracy is improved, but processing speed decreases
Solution Approach 1:
The system dynamically adjusts the depth and type of analysis applied to each message based on risk indicators. Messages with high-risk characteristics (unusual senders, suspicious patterns) receive comprehensive analysis with multiple verification techniques, while normal messages receive expedited processing with fewer checks. This dynamic adaptation maintains high detection accuracy for suspicious messages while preserving overall processing speed.
Solution Approach 2:
The system performs preliminary analysis on all messages using fast, lightweight techniques (header validation, sender reputation checks, basic pattern matching) before routing messages to appropriate deep-analysis queues. This preliminary filtering ensures that only messages requiring intensive analysis are subjected to comprehensive checks, maintaining both speed and accuracy.
Data Source
AI summary
Aspects of the disclosure relate to detecting impersonation in email body content using machine learning. Based on email data received from user accounts, a computing platform may generate user identification models that are each specific to one of the user accounts. The computing platform may intercept a message from a first user account to a second user account and may apply a user identification model, specific to the first user account, to the message, so as to calculate feature vectors for the message. The computing platform then may apply impersonation algorithms to the feature vectors and may determine that the message is impersonated. Based on results of the impersonation algorithms, the computing platform may modify delivery of the message.