A systematic review of fuzzing

被引:6
|
作者
Zhao, Xiaoqi [1 ]
Qu, Haipeng [2 ]
Xu, Jianliang [2 ]
Li, Xiaohui [2 ]
Lv, Wenjie [2 ]
Wang, Gai-Ge [2 ]
机构
[1] Qingdao Univ Technol, Sch Informat & Control Engn, Qingdao, Peoples R China
[2] Ocean Univ China, Coll Comp Sci & Technol, Qingdao, Peoples R China
基金
中国国家自然科学基金;
关键词
Fuzzing; Software testing; Security; Survey; Vulnerability; NETWORK;
D O I
10.1007/s00500-023-09306-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Fuzzing is an important technique in software and security testing that involves continuously generating a large number of test cases against target programs to discover unexpected behaviors such as bugs, crashes, and vulnerabilities. Recently, fuzzing has advanced considerably owing to the emergence of new methods and corresponding tools. However, it still suffers from low coverage, ineffective detection of specific vulnerabilities, and difficulty in deploying complex applications. Therefore, to comprehensively survey the development of fuzzing techniques and analyze their advantages and existing challenges, this paper provides a comprehensive survey of the development of fuzzing techniques, summarizes the main research issues, and provides a categorized overview of the latest research advances and applications. The paper first introduces the background and related work on fuzzing. Research issues are subsequently addressed and summarized, along with the latest research developments. Furthermore, various customized fuzzing techniques in different applications are presented. Finally, the paper discusses future research directions.
引用
收藏
页码:5493 / 5522
页数:30
相关论文
共 50 条
  • [1] A systematic review of fuzzing
    Xiaoqi Zhao
    Haipeng Qu
    Jianliang Xu
    Xiaohui Li
    Wenjie Lv
    Gai-Ge Wang
    Soft Computing, 2024, 28 : 5493 - 5522
  • [2] A systematic review of fuzzing techniques
    Chen, Chen
    Cui, Baojiang
    Ma, Jinxin
    Wu, Runpu
    Guo, Jianchao
    Liu, Wenqian
    COMPUTERS & SECURITY, 2018, 75 : 118 - 137
  • [3] A systematic review of fuzzing based on machine learning techniques
    Wang, Yan
    Jia, Peng
    Liu, Luping
    Huang, Cheng
    Liu, Zhonglin
    PLOS ONE, 2020, 15 (08):
  • [4] Fuzzing drones for anomaly detection: A systematic literature review
    Malviya, Vikas K.
    Minn, Wei
    Shar, Lwin Khin
    Jiang, Lingxiao
    COMPUTERS & SECURITY, 2025, 148
  • [5] A Review of Fuzzing Techniques
    Ren Z.
    Zheng H.
    Zhang J.
    Wang W.
    Feng T.
    Wang H.
    Zhang Y.
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2021, 58 (05): : 944 - 963
  • [6] Vulnerability detection through machine learning-based fuzzing: A systematic review
    Chafjiri, Sadegh Bamohabbat
    Legg, Phil
    Hong, Jun
    Tsompanas, Michail-Antisthenis
    COMPUTERS & SECURITY, 2024, 143
  • [7] Systematic Fuzzing and Testing of TLS Libraries
    Somorovsky, Juraj
    CCS'16: PROCEEDINGS OF THE 2016 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, 2016, : 1492 - 1504
  • [8] Embedded fuzzing: a review of challenges, tools, and solutions
    Max Eisele
    Marcello Maugeri
    Rachna Shriwas
    Christopher Huth
    Giampaolo Bella
    Cybersecurity, 5
  • [9] Embedded fuzzing: a review of challenges, tools, and solutions
    Eisele, Max
    Maugeri, Marcello
    Shriwas, Rachna
    Huth, Christopher
    Bella, Giampaolo
    CYBERSECURITY, 2022, 5 (01)
  • [10] Towards Systematic and Dynamic Task Allocation for Collaborative Parallel Fuzzing
    Pham, Van-Thuan
    Nguyen, Manh-Dung
    Ta, Quang-Trung
    Murray, Toby
    Rubinstein, Benjamin I. P.
    2021 36TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING ASE 2021, 2021, : 1337 - 1341