Adaptive LLM-Driven Phishing Defense Using Real-Time Psychological Cue Detection

Abstract

Phishing attacks remain one of the most prevalent and damaging cybersecurity threats, with attackers increasingly employing sophisticated psychological manipulation techniques to deceive victims. This paper introduces an innovative adaptive defense system that leverages Large Language Models (LLMs) and real-time psychological cue detection to enhance phishing threat detection and mitigation. By integrating psychological cues, such as urgency, fear, and social influence, detected from email and web communication patterns, the system dynamically adapts its defense strategies in real-time. The proposed framework continuously analyzes both the content and context of messages, using LLMs to assess linguistic features, detect inconsistencies, and identify manipulative tactics employed by cybercriminals. A psychological cue-based risk model is developed, enabling the system to predict the likelihood of phishing attacks based on the emotional tone and behavioral triggers embedded within the communication. The effectiveness of the approach is demonstrated through experimental results, showing a significant improvement in phishing detection accuracy and reduced false positive rates compared to traditional methods. This adaptive, AI-driven model provides a robust solution for defending against the evolving landscape of phishing attacks, offering both proactive and reactive capabilities in real-world environments.