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 sta...

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Vydáno v:	IEEE internet of things journal Ročník 12; číslo 17; s. 34753 - 34767
Hlavní autoři:	Chen, Xiao, Sha, Letian, Gao, Qingguan, Xiao, Fu, Pan, Jiaye
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.09.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Abstract syntax tree (AST) Accuracy binary code similarity detection (BCSD) Binary codes buffer overflow (BOF) Buffer overflows Buffers Carbon Carbon footprint Computer architecture Deep learning Devices Emissions encoder representations from transformer (BERT) Energy consumption Firmware Flow graphs Footprint analysis Industrial applications Industrial Internet of Things Industrial Internet of Things (IIoT) Internet of Things Logic Microprogramming reaching definition analysis Semantics siamese network Static analysis Syntactics
ISSN:	2327-4662, 2327-4662
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Abstract	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 article, 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 multiarchitecture 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 ten unknown critical vulnerabilities, all of which have been recognized by common vulnerabilities and exposure and covered by media.
AbstractList	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 article, 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 multiarchitecture 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 ten unknown critical vulnerabilities, all of which have been recognized by common vulnerabilities and exposure and covered by media.
Author	Sha, Letian Chen, Xiao Gao, Qingguan Xiao, Fu Pan, Jiaye
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SubjectTerms	Abstract syntax tree (AST) Accuracy binary code similarity detection (BCSD) Binary codes buffer overflow (BOF) Buffer overflows Buffers Carbon Carbon footprint Computer architecture Deep learning Devices Emissions encoder representations from transformer (BERT) Energy consumption Firmware Flow graphs Footprint analysis Industrial applications Industrial Internet of Things Industrial Internet of Things (IIoT) Internet of Things Logic Microprogramming reaching definition analysis Semantics siamese network Static analysis Syntactics
Title	Boreas: Semantic-Aware Framework for Buffer Overflow Detection to Reduce Carbon Footprint in IoT Devices
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