MAFUZZ: Adaptive Gradient-Guided Fuzz Testing for Satellite Internet Ground Terminals

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書誌詳細
出版年:	Electronics vol. 14, no. 16 (2025), p. 3168-3189
第一著者:	Cao, Ang
その他の著者:	Zhao, Yongli, Yan, Xiaodan, Wang, Wei, Yang, Jian, Zhang, Yuanjian, Liu, Ruiqi
出版事項:	MDPI AG
主題:	Software Source code Neural networks Microwave communications Internet access Internet Static models Linux Computer terminals Logic Firmware Design Malware Satellite constellations Automation Machine learning Mutation Satellites Internet of Things Interfaces Modems Software testing
オンライン･アクセス:	Citation/Abstract Full Text + Graphics Full Text - PDF
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その他の書誌記述
抄録:	With the proliferation of satellite internet systems, such as Starlink and OneWeb, ground terminals have become critical for ensuring end-user connectivity. However, the security of Satellite Internet Ground Terminals (SIGTs) remains underexplored. These Linux-based embedded systems are vulnerable to advanced attacks due to limited source code access and immature protection mechanisms. This paper presents MAFUZZ, an adaptive fuzzing framework guided by neural network gradients to uncover hidden vulnerabilities in SIGT binaries. MAFUZZ uses a lightweight machine learning model to identify input bytes that influence program behavior and applies gradient-based mutation accordingly. It also integrates an adaptive Havoc mechanism to enhance path diversity. We compare MAFUZZ with NEUZZ, a neural fuzzing tool that uses program smoothing to guide mutation through a static model. Our experiments on real-world Linux binaries show that MAFUZZ improves path coverage by an average of 17.4% over NEUZZ, demonstrating its effectiveness in vulnerability discovery and its practical value for securing satellite terminal software.
ISSN:	2079-9292
DOI:	10.3390/electronics14163168
ソース:	Advanced Technologies & Aerospace Database