Email Impersonation Detection Using Sender Language Modeling
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Solution Overview
Problem
Existing systems lack effective methods to detect and remediate electronic impersonation attacks in email communications, particularly in identifying compromised email accounts and preventing malicious emails from being sent.
Innovation Solution
A method involving the creation of a sender model based on language signals from a user's email account, which analyzes the language patterns in outgoing emails and compares them to a threshold similarity score to determine authenticity, flagging or quarantining emails that deviate from the expected patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional email filtering systems are used, then basic spam detection is provided, but they cannot effectively detect electronic impersonation attacks or identify compromised email accounts
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical email data from a user's account before the attack occurs. It builds a baseline model of the user's authentic email writing patterns, including language style, sentence structure, and communication habits. This preliminary modeling enables the system to detect deviations that indicate impersonation attacks.
Solution Approach 2:
The system introduces an intermediary analysis layer that sits between the email sender and recipient. This intermediary component compares incoming emails against the pre-established baseline model and generates similarity scores, acting as a mediator that identifies compromised accounts without requiring direct intervention from the user or recipient.
2Measurement precision
If language pattern analysis is implemented to detect impersonation, then detection precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by focusing analysis only on the most critical language features and patterns that distinguish authentic from impersonated emails. Rather than analyzing every aspect of an email, it concentrates computational resources on key indicators such as writing style, sentence structure, and vocabulary usage, achieving high detection precision with reduced processing overhead.
Solution Approach 2:
The system creates a simplified copy or representation of the user's authentic email patterns in the baseline model. This copied representation captures the essential characteristics of genuine emails without requiring the full complexity of the original communication data, enabling fast comparison and detection while maintaining high precision.
3Object-affected harmful factors
If real-time analysis of outgoing emails is performed, then malicious emails are prevented from being sent, but system resource consumption increases
Solution Approach 1:
The system applies preliminary anti-action by pre-establishing the baseline model of authentic email patterns before attacks occur. This preparation allows the system to quickly compare incoming emails against the baseline using efficient similarity scoring algorithms, preventing malicious emails from being sent while minimizing real-time computational energy consumption.
Solution Approach 2:
The system uses computationally inexpensive similarity scoring methods that can be applied rapidly to each outgoing email. Rather than employing heavy machine learning models for every email analysis, it uses lightweight comparison algorithms that consume minimal energy and computational resources while effectively identifying impersonation attempts.
Data Source
AI summary
A method includes: accessing a corpus of emails sent from a email account prior to the initial time period; correlating sequences of words, in the corpus of emails, with language signals; aggregating the language signals into a sender model that represents combinations of language signals characteristic of language in emails sent from the email account; later, accessing a email outbound from the email account and directed to a recipient; scanning the email for the set of language signals; correlating sequences of words in the email with language signals; calculating a similarity score for the email based on the subset of language signals detected in the email and the sender model; and, in response to the similarity score falling below a threshold similarity, flagging the email as suspicious and redirecting the email away from the recipient.