DEGNN: A Deep Learning-Based Method for Unmanned Aerial Vehicle Software Security Analysis - Jiang Du; Qiang Wei; Yisen Wang; Xingyu Bai (Drones)
With the increasing utilization of drones, the cyber security threats they face have become more prominent. Code reuse in the software development of drone systems has led to vulnerabilities in drones. The binary code similarity analysis method offers a way to analyze drone firmware lacking source code. This paper proposes DEGNN, a novel graph neural network for binary code similarity analysis. It uses call-enhanced control graphs and attention mechanisms to generate dual embeddings of functions and predict similarity based on graph structures and node features. DEGNN is effective in cross-architecture tasks. Experimental results show that in the cross-architecture binary function search, DEGNN’s mean reciprocal rank and recall@1 surpass the state of the art by 12% and 28.6%, respectively. In the cross-architecture real-world vulnerability search, specifically targeting drone systems, it has a 33.3% performance improvement over the SOTA model, indicating its great potential in enhancing drone cyber security.