Email Plugin Phishing Detection via Source Code Analysis
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Solution Overview
Problem
Regular employees often fail to detect phishing emails, exposing networks and computers to exploitation due to the complexity of advanced phishing tactics, despite security training, highlighting a need for sophisticated email analysis tools.
Innovation Solution
A plugin for email clients using a sophisticated analysis engine and algorithms to detect abnormalities in emails, such as sender IP location, domain checks, URL analysis, and threat intelligence, providing a visible warning system to users about potential phishing risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If employees receive security training, then their awareness of phishing risks improves, but they still fail to detect advanced phishing emails
Solution Approach 1:
The patent introduces an intermediary analysis engine that acts as a mediator between the employee and the phishing email. This engine automatically performs sophisticated analysis of email characteristics (sender IP location, domain registration data, URL analysis, threat intelligence) without requiring the employee to understand these complex factors. The intermediary translates complex security analysis into simple visual indicators (phish-o-meter readings), resolving the contradiction by providing expert-level detection capability without requiring expert knowledge from the user.
Solution Approach 2:
The system enables self-service by automatically performing security analysis without human intervention in the detection process. The analysis engine autonomously evaluates email characteristics, checks threat intelligence databases, and generates phishing probability assessments. This allows regular employees to benefit from automated expert analysis rather than requiring them to manually perform complex security evaluations, thereby improving detection reliability without increasing operational complexity for users.
2Reliability
If sophisticated analysis algorithms are implemented, then phishing detection capability improves, but system resource consumption increases
Solution Approach 1:
The system applies partial action by selectively analyzing only the most relevant email characteristics using the phish-o-meter methodology. Rather than performing exhaustive analysis of all possible email attributes, the system focuses on key indicators (sender IP location, domain registration timing, URL structure, threat intelligence matches) that provide the highest detection value. This selective approach maintains high detection accuracy while reducing unnecessary computational resource consumption.
3Measurement precision
If multiple phishing indicators are analyzed, then detection precision improves, but analysis time increases
Solution Approach 1:
The system performs preliminary action by pre-loading and caching threat intelligence data, domain reputation information, and IP location databases before they are needed for analysis. The analysis engine has access to pre-computed threat intelligence and domain registration data, eliminating the need to query external databases in real-time during email analysis. This preliminary preparation maintains high detection precision across multiple indicators while significantly reducing the time required to analyze incoming emails.
Data Source
AI summary
An application or a plugin to Email client application such as Outlook or Thunderbird is described which automatically analyzes and detects Phishing emails. The application reads emails in an email client that an end user is using to access his/her emails. The application views source code of the email with all email headers, analyzes signatures, SMTP logs, and all traces of email origin.