Network Protocol Fuzzing: Method Classification and Research Progress

LAI Guoqing; ZHU Yuefei; CHEN Di; LU Bin; LIU Long; ZHANG Zihao

doi:10.11999/JEIT250188

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LAI Guoqing, ZHU Yuefei, CHEN Di, LU Bin, LIU Long, ZHANG Zihao. Network Protocol Fuzzing: Method Classification and Research Progress[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250188

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LAI Guoqing, ZHU Yuefei, CHEN Di, LU Bin, LIU Long, ZHANG Zihao. Network Protocol Fuzzing: Method Classification and Research Progress[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250188

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Network Protocol Fuzzing: Method Classification and Research Progress

doi: 10.11999/JEIT250188 cstr: 32379.14.JEIT250188

LAI Guoqing^{1, 2},
ZHU Yuefei^{1, 2
,
,},
CHEN Di²,
LU Bin²,
LIU Long²,
ZHANG Zihao²

1.
Henan Key Laboratory of Network Cryptography Technology, Information Engineering University, Zhengzhou 450001, China
2.
Key Laboratory of Cyberspace Security of Ministry of Education, Information Engineering University, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China(62302520, 62402524)

Received Date: 2025-03-21
Rev Recd Date: 2025-08-20

Available Online: 2025-08-27

Abstract

Abstract

Significance Network security vulnerabilities arising from flaws in network protocol implementations cause substantial losses and adverse societal effects, exemplified by the Heartbleed vulnerability in OpenSSL (CVE-2014-0160). This flaw allows attackers to extract encryption keys from public servers, enabling decryption of traffic or unauthorized authentication on less secure systems. Approximately 500,000 internet servers are affected when the vulnerability is publicly disclosed. Against this backdrop of escalating network security risks, ensuring the security of network protocol software becomes a critical research area. Fuzzing, a software testing technique, emerges as one of the most widely used approaches for identifying vulnerabilities in network protocol implementations due to its ease of deployment and high efficiency. The core concept of fuzzing is to improve software security by generating and sending crafted test cases to the target program. Despite significant progress in this field, Network Protocol Fuzzing (NPF) still faces technical challenges. Currently, no systematic and up-to-date review of NPF research exists, limiting researchers’ ability to grasp recent advances. This paper conducts a comprehensive review of NPF techniques, aiming to provide researchers in the field of network protocol security with valuable references for tool selection and optimization. Progress Since the proposal of AFLNET in 2020, considerable progress occurs in the field of NPF, addressing key challenges such as protocol packet construction, protocol state awareness, and network communication efficiency optimization. Specifically, for protocol packet construction, researchers propose machine learning-based packet generation methods that integrate both generation and mutation strategies. Mutation operators and guidance techniques are designed to target specific protocol characteristics. In terms of protocol state awareness, state tracking capabilities are enhanced through state variable capture and state model construction. Furthermore, state fuzzing has been widely employed to detect state machine bugs. For protocol stack efficiency optimization, researchers improve testing efficiency by refining communication mechanisms and applying snapshot techniques. Conclusions To comprehensively summarize the research progress in NPF, this paper first clarifies the unique characteristics of network protocol software compared with other fuzzing targets. These characteristics include strict protocol message formats, asynchronous network interactions, and complex protocol state maintenance. A problem-oriented classification framework for NPF is proposed, structured around three core challenges: protocol packet construction, protocol state awareness, and protocol stack efficiency optimization. Based on this framework, research advancements in NPF over the past eight years are systematically reviewed, with a technical analysis of the capabilities, and limitations of existing approaches. This review highlights several key challenges in the field. For input construction, major limitations include weak validity of generated inputs, input space explosion, and restrictions imposed by encryption and authentication mechanisms. In terms of state awareness, the field faces insufficient protocol state space exploration and low levels of test intelligence and automation. Regarding performance optimization, technical challenges include slow network communication speed, limited scalability across different protocol implementations, and inadequate adaptability to complex network environments. This study provides both theoretical foundations and practical references to guide future research and technological development in the NPF domain. Prospects Future research in NPF can integrate emerging technologies such as Artificial Intelligence (AI) to enhance the intelligence and automation of testing processes. For example, combining AI methods with program analysis techniques may enable a deeper understanding of protocol behavior and more efficient generation of test packets. Developing state representations tailored to different protocol characteristics, implementing real-time protocol state mapping, and applying state prediction and reasoning based on LLM can further improve the efficiency and applicability of NPF tools. In addition, introducing technologies such as parallelization, distributed computing, modular test architectures, and integrated network simulation with virtualization can significantly enhance testing scalability and adaptability. Through the integration of emerging technologies and interdisciplinary research, NPF is expected to play an increasingly critical role in network protocol security, providing robust technical support for building secure and reliable network environments.
- Network protocol software security,
- Vulnerability discovery,
- Fuzzing,
- Automated software testing

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References(80)

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