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

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Publicado en:Electronics vol. 14, no. 16 (2025), p. 3168-3189
Autor principal: Cao, Ang
Otros Autores: Zhao, Yongli, Yan, Xiaodan, Wang, Wei, Yang, Jian, Zhang, Yuanjian, Liu, Ruiqi
Publicado:
MDPI AG
Materias:
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
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Resumen: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
Fuente:Advanced Technologies & Aerospace Database