AI-Generated Phishing Email Detection with Composite Scoring
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Solution Overview
Problem
Existing email security solutions struggle to effectively detect and mitigate AI-generated phishing emails, which are highly sophisticated and difficult to distinguish from genuine communications due to their professional appearance and personalized content.
Innovation Solution
A system and method that combines static attribute evaluation, trust graph analysis, and AI models to generate reputation, trust, and certainty scores, followed by summary comparison with known malicious emails to block or mark suspicious messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI-generated phishing emails are used, then the sophistication and convincing nature of the attack increases, but the difficulty of detection increases
Solution Approach 1:
The detection system segments the email analysis into multiple independent components: static attribute evaluation (headers, URLs, attachments), trust graph analysis (sender-receiver relationships), AI detection analysis (LLM-based generative AI detection), and summary comparison. Each component generates independent scores that are combined to make the final detection decision, allowing the system to handle sophisticated AI-generated emails through multi-faceted analysis
Solution Approach 2:
The system creates a composite detection approach by combining multiple detection methodologies (static analysis, trust graph, AI detection, summary comparison) into a unified scoring system. The reputation score, trust score, certainty score, and match score are weighted and combined to produce an overall phishing probability, creating a robust composite detection mechanism that addresses the sophistication of AI-generated emails
2Reliability
If multiple detection methods are combined, then the detection accuracy improves, but the system complexity increases
Solution Approach 1:
The system implements dynamic scoring where the weight of each detection component can be adjusted based on the specific email characteristics. The reputation score, trust score, certainty score, and match score are dynamically weighted and combined, allowing the system to adapt to different types of phishing attempts while maintaining a manageable overall structure through standardized scoring mechanisms
Data Source
AI summary
Systems and methods for detecting a phishing attack in an email message. The method includes intercepting and evaluating an email, and generating a reputation score based on the evaluation of the email message. If the malicious component is detected, the email is blocked, if not, a trust score is generated. A determination is made whether the email is generated by AI, based on LLM. A certainty score is generated which is indicative of intentions and context of the email message being malicious. When the certainty score is higher than a threshold, a combined score by combining the reputation score, the trust score, and the certainty score. When the combined reputation score is higher than a threshold, the email is blocked, and if lower, a summary is generated by a summary AI engine. Based on a comparison with known malware summaries, the email is flagged or blocked.