MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection
📰 ArXiv cs.AI
Learn how MultiPhishGuard, a multi-agent LLM system, detects phishing emails using explainable and adaptive techniques, improving security and reducing false positives.
Action Steps
- Implement a multi-agent system using LLMs to detect phishing emails
- Train the system on a diverse dataset of phishing and legitimate emails
- Use explainable techniques to analyze and understand the detection results
- Adapt the system to novel phishing strategies using online learning and feedback mechanisms
- Evaluate the system's performance using metrics such as accuracy, precision, and recall
Who Needs to Know This
Security teams and developers working on email security solutions can benefit from this research to improve their phishing detection systems. The explainable and adaptive nature of MultiPhishGuard can help reduce false positives and improve overall security.
Key Insight
💡 Explainable and adaptive multi-agent LLM systems can improve phishing email detection by adapting to novel and rapidly changing phishing strategies.
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Introducing MultiPhishGuard: an explainable & adaptive multi-agent LLM system for phishing email detection #phishingdetection #emailsecurity #LLM
Key Takeaways
Learn how MultiPhishGuard, a multi-agent LLM system, detects phishing emails using explainable and adaptive techniques, improving security and reducing false positives.
Full Article
Title: MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection
Abstract:
arXiv:2505.23803v2 Announce Type: replace-cross Abstract: Phishing email detection faces significant challenges due to evolving adversarial tactics and heterogeneous attack patterns. Traditional approaches, such as rule-based filters and denylists, often struggle to keep pace, leading to missed detections and security risks. While machine learning methods have improved detection performance, they remain limited in adapting to novel and rapidly changing phishing strategies. We present MultiPhishG
Abstract:
arXiv:2505.23803v2 Announce Type: replace-cross Abstract: Phishing email detection faces significant challenges due to evolving adversarial tactics and heterogeneous attack patterns. Traditional approaches, such as rule-based filters and denylists, often struggle to keep pace, leading to missed detections and security risks. While machine learning methods have improved detection performance, they remain limited in adapting to novel and rapidly changing phishing strategies. We present MultiPhishG
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