EXPLAINABLE AI FOR CYBER THREAT INTELLIGENCE USING LARGE LANGUAGE MODEL ARCHITECTURE
Keywords:
Explainable AI, Cyber Threat Intelligence, Large Language Models, Natural Language Processing, Real-world DatasetsAbstract
Detection and understanding of the threats are playing important roles in setting up any kind of defense strategy; hence, increasing detection capability, along with contextual insight in today’s dynamic world of cyber security, is very much critical. This paper advances an understanding of Cyber Threat Intelligence using Large Language Model Architectures for Explainable Artificial Intelligence. Our approach leverages the LLM’s superior NLP to analyze vast amounts of threat data and provide actionable, understandable insights into possible security risks. We introduce a new paradigm whereby the integration of LLMs into classic CTI frameworks enables complex threat pattern identification and provides human-readable explanations for each detected threat. This will enhance the transparency and trustworthiness of AI-driven threat analysis, thus making decision-making easier and more informed by cybersecurity professionals. Extensive testing was conducted on real-world datasets to validate our approach, indicating that our approach significantly improves threat detection accuracy and explanation quality compared to the current methods. These findings suggest that LLMs dramatically improve cybersecurity tool efficacy by embedding the same into CTI systems for new frontiers toward resilience and adaptiveness.
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Copyright (c) 2021 Rajashekhar Reddy Kethireddy (Author)
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