A Survey of Protocol Fuzzing

被引:0
|
作者
Zhang, Xiaohan [1 ,2 ,3 ]
Zhang, Cen [4 ]
Li, Xinghua [1 ,2 ,3 ]
Du, Zhengjie [5 ]
Mao, Bing [5 ]
Li, Yuekang [4 ]
Zheng, Yao wen [4 ]
Li, Yeting [6 ]
Pan, Li [7 ]
Liu, Yang [4 ]
Deng, Robert [8 ]
机构
[1] Minist Educ, State Key Lab Integrated Serv Networks, Xian, Peoples R China
[2] Minist Educ, Engn Res Ctr Big Data Secur, Xian, Peoples R China
[3] Xidian Univ, Sch Cyber Engn, Xian, Peoples R China
[4] Nanyang Technol Univ, Singapore, Singapore
[5] Nanjing Univ, Nanjing, Peoples R China
[6] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China
[7] Shanghai Jiao Tong Univ, Shanghai, Peoples R China
[8] Singapore Management Univ, Singapore, Singapore
基金
新加坡国家研究基金会; 中国国家自然科学基金;
关键词
Protocol; fuzz testing; security; NETWORK PROTOCOL; SYMBOLIC EXECUTION; STATE; IMPLEMENTATIONS; SECURITY;
D O I
10.1145/3696788
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Communication protocols form the bedrock of our interconnected world, yet vulnerabilities within their implementations pose significant security threats. Recent developments have seen a surge in fuzzing-based research dedicated to uncovering these vulnerabilities within protocol implementations. However, there still lacks a systematic overview of protocol fuzzing for answering the essential questions such as what the unique challenges are, how existing works solve them, and so on. To bridge this gap, we conducted a comprehensive investigation of related works from both academia and industry. Our study includes a detailed summary of the specific challenges in protocol fuzzing and provides a systematic categorization and overview of existing research efforts. Furthermore, we explore and discuss potential future research directions in protocol fuzzing.
引用
收藏
页数:36
相关论文
共 50 条
  • [31] Green-Fuzz: Efficient Fuzzing for Network Protocol Implementations
    Andarzian, Seyed Behnam
    Daniele, Cristian
    Poll, Erik
    FOUNDATIONS AND PRACTICE OF SECURITY, PT I, FPS 2023, 2024, 14551 : 253 - 268
  • [32] A Vulnerability Mining System Based on Fuzzing for IEC 61850 Protocol
    Tu, Tengfei
    Zhang, Hua
    Qin, Boqin
    Chen, Zhuo
    PROCEEDINGS OF THE 2017 5TH INTERNATIONAL CONFERENCE ON FRONTIERS OF MANUFACTURING SCIENCE AND MEASURING TECHNOLOGY (FMSMT 2017), 2017, 130 : 589 - 597
  • [33] Inferring OpenVPN State Machines Using Protocol State Fuzzing
    Daniel, Lesly-Ann
    de Ruiter, Joeri
    Poll, Erik
    2018 3RD IEEE EUROPEAN SYMPOSIUM ON SECURITY AND PRIVACY WORKSHOPS (EUROS&PW 2018), 2018, : 11 - 19
  • [34] MSGFuzzer: Message Sequence Guided Industrial Robot Protocol Fuzzing
    Zhang, Yang
    Fang, Dongliang
    Liu, Puzhuo
    Xi, Laile
    Lu, Xiao
    Chen, Xin
    Si, Shuaizong
    Sun, Limin
    2024 IEEE CONFERENCE ON SOFTWARE TESTING, VERIFICATION AND VALIDATION, ICST 2024, 2024, : 140 - 150
  • [35] GANFuzz: A GAN-based industrial network protocol fuzzing framework
    Hu, Zhicheng
    Shi, Jianqi
    Huang, YanHong
    Xiong, Jiawen
    Bu, Xiangxing
    2018 ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS, 2018, : 138 - 145
  • [36] Grammar-based Adaptive Fuzzing: Evaluation on SCADA Modbus Protocol
    Yoo, Hyunguk
    Shon, Taeshik
    2016 IEEE INTERNATIONAL CONFERENCE ON SMART GRID COMMUNICATIONS (SMARTGRIDCOMM), 2016,
  • [37] Robustness Evaluation of Cyber Physical Systems through Network Protocol Fuzzing
    Ananda, Tulasi K.
    Simran, Gitanjali T.
    Sukumara, T.
    Sasikala, D.
    Kumar, Ramakanth P.
    PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING & COMMUNICATION ENGINEERING (ICACCE-2019), 2019,
  • [38] Fuzzing frameworks for server-side web applications: a survey
    Dharmaadi, I. Putu Arya
    Athanasopoulos, Elias
    Turkmen, Fatih
    INTERNATIONAL JOURNAL OF INFORMATION SECURITY, 2025, 24 (02)
  • [39] Fuzzing vulnerability discovery techniques: Survey, challenges and future directions
    Beaman, Craig
    Redbourne, Michael
    Mummery, J. Darren
    Hakak, Saqib
    COMPUTERS & SECURITY, 2022, 120
  • [40] SeqFuzzer: An Industrial Protocol Fuzzing Framework from a Deep Learning Perspective
    Zhao, Hui
    Li, Zhihui
    Wei, Hansheng
    Shi, Jianqi
    Huang, Yanhong
    2019 IEEE 12TH CONFERENCE ON SOFTWARE TESTING, VALIDATION AND VERIFICATION (ICST 2019), 2019, : 59 - 67