Research on Intelligent Firmware Vulnerability Detection and Priority Assessment Method Based on Hybrid Analysis

Binary firmware underpins critical infrastructure but often contains vulnerabilities that conventional detection mechanisms fail to identify. In this work, we develop a hybrid analytical framework that integrates static pattern extraction with runtime behavioral monitoring, achieving detection rates of 93.7% across a corpus of 40 million procedures collected from production firmware. Static pattern recognition leverages control flow graph embeddings, while probabilistic scoring quantifies contextual risk. Cross-architecture evaluation across ARM, MIPS, x86, and PowerPC demonstrates robustness against variations in compilation. Our methodology also uncovers zero-day vulnerabilities, and the computational overhead remains manageable for deployment on resource-constrained platforms, reducing false positive rates by 56.7% compared to existing approaches.