Boreas: Semantic-Aware Framework for Buffer Overflow Detection to Reduce Carbon Footprint in IoT Devices

The rapid development of the Industrial Internet of Things (IIoT) has raised concerns about device security and energy consumption. The widespread presence of buffer overflow (BOF) vulnerabilities in IoT devices not only threatens devices but also leads to increased carbon emissions. Traditional static analysis methods suffer from low accuracy and high costs. Mainstream binary code similarity detection (BCSD) methods are mainly based on control flow graphs (CFGs) or instructions, which often fail to effectively capture semantic information. In this paper, we propose Boreas, a semantic-aware BOF vulnerability detection framework to reduce the carbon footprint in IoT devices. The abstract syntax tree (AST) is employed to achieve precise semantic representation of multi-architecture code. By considering the often-overlooked implicit data receiving points, Boreas comprehensively locates vulnerable binaries in firmware. By leveraging backward reaching definition analysis, Boreas removes extensive code irrelevant to BOF to simplify ASTs, enables cross-function/file detection and enhances interpretability for BCSD. Additionally, we develop a deep learning model AST-BERT for BCSD, which effectively transforms simplified ASTs into vectors. Based on the distance between target and vulnerable vectors, the accurate BOF detection is achieved. To evaluate the performance of Boreas, we construct a large-scale dataset containing real-world IoT firmware. Experiments show that Boreas outperforms state-of-the-art BCSD methods with the precision of 87.08% and leading static analysis tools with the F1-score of 88.81% in vulnerability detection. Finally, Boreas successfully discovers 10 unknown critical vulnerabilities, all of which have been recognized by CVE and covered by media.