Knowledge Injection of Structural CyberSecurity Concept into Large Language Models

This study presents a novel approach to enhance Large Language Models’ (LLMs) capabilities in cybersecurity analysis through two main methods. First, we propose a Structure-guided Enhancement Network that leverages Finite State Machine (FSM) representations to guide the attention mechanism in processing API call sequences for malware classification. The guided-attention mechanism integrates structural FSM features to enhance the attention computation on API sequences. Second, we develop a Multi-modal Fusion System that aligns assembly code and API call sequences from the same malware through cross-attention mechanisms, creating a shared latent space for improved context generation. Our research demonstrates how structural guidance and multi-modal fusion can enhance LLMs’ performance in cybersecurity-specific tasks.