LLM-Based Advanced Persistent Threat Attribution: A Novel Framework for Enhanced Cyber Threat Intelligence

Abstract

Advanced Persistent Threats (APTs) represent one of the most sophisticated challenges in modern cybersecurity, requiring innovative approaches for accurate attribution. This paper introduces the LLM-Based Advanced Persistent Threat Attribution Framework (LLMAPT), a novel approach that leverages Large Language Models (LLMs) to enhance APT attribution capabilities. The framework addresses critical limitations in existing attribution methods by implementing a multi-layered architecture that includes multi-source intelligence integration, LLM-powered semantic analysis, structured attribution reasoning, calibrated confidence quantification, and explainable attribution interfaces. Key innovations include chain-of-thought attribution reasoning, adversarial robustness mechanisms, calibrated uncertainty quantification, temporal evolution modeling, and multi-level explainability. Experimental evaluations demonstrate that LLMAPT significantly outperforms traditional attribution methods and earlier machine learning approaches across multiple performance metrics. The framework provides security analysts with more accurate, robust, and explainable attribution capabilities, enabling more effective response to sophisticated cyber threats.