Increasing Reliability of Programming Interfaces Based on Fuzz Testing

被引:0
|
作者
Khortiuk, Yaroslav [1 ]
Kondratenko, Galyna [1 ]
Sidenko, Ievgen [1 ]
Kondratenko, Yuriy [1 ]
机构
[1] Petro Mohyla Black Sea Natl Univ, Intelligent Informat Syst Dept, Mykolaiv, Ukraine
来源
2020 IEEE 11TH INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS, SERVICES AND TECHNOLOGIES (DESSERT): IOT, BIG DATA AND AI FOR A SAFE & SECURE WORLD AND INDUSTRY 4.0 | 2020年
关键词
fuzzing; fuzz testing; automation; quality assurance; REST API;
D O I
10.1109/dessert50317.2020.9125060
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Over the last decade, there has been a significant growth in web applications for data processing and output, most of them working through the REST API to communicate with third-party applications. Tools for automatically testing web services through their REST API and verifying the reliability and security of these services are still in their infancy. The most sophisticated testing tools currently available for the REST API scan all API traffic in real-time and then analyze, modify, and reproduce it. Many of these tools were born as extensions of more established web site testing and crawling tools. As these REST API testing tools are all recent and not widely used, it is unknown at this time how effective they are in finding errors and how important they are for security. In this paper, using the latest researches in the field, several methods and approaches for fuzzing web interfaces are analyzed. Their comparative analysis of existing techniques allows to see the current state, performance, and appliance to real-world web application and widely used REST API architecture in general.
引用
收藏
页码:272 / 277
页数:6
相关论文
共 50 条
  • [21] Binary-oriented Hybrid Fuzz Testing
    Dong Fangquan
    Dong Chaoqun
    Zhang Yao
    Lin Teng
    PROCEEDINGS OF 2015 6TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE, 2015, : 345 - 348
  • [22] Key Data Location Method for Fuzz Testing Based on Path Label and Data Mutation
    Jiao L.-L.
    Luo S.-L.
    Liu W.-T.
    Pan L.-M.
    Pan, Li-Min (panlimin2016@gmail.com), 1600, Beijing Institute of Technology (40): : 1009 - 1017
  • [23] Dynamic fuzz testing of UAV configuration parameters based on dual guidance of fitness and coverage
    Ma, Yuexuan
    Yu, Xiao
    Zhang, Li
    Li, Zhao
    Li, Yuanzhang
    Tan, Yu-an
    CONNECTION SCIENCE, 2024, 36 (01)
  • [24] DeltaFuzz: Historical Version Information Guided Fuzz Testing
    Jia-Ming Zhang
    Zhan-Qi Cui
    Xiang Chen
    Huan-Huan Wu
    Li-Wei Zheng
    Jian-Bin Liu
    Journal of Computer Science and Technology, 2022, 37 : 29 - 49
  • [25] Fuzz Testing Virtual ECUs as Part of the Continuous Security Testing Process
    Oka D.K.
    SAE International Journal of Transportation Cybersecurity and Privacy, 2020, 2 (02): : 159 - 168
  • [26] CRAXfuzz: Target-Aware Symbolic Fuzz Testing
    Yeh, Chao-Chun
    Chung, Hsiang
    Huang, Shih-Kun
    39TH ANNUAL IEEE COMPUTERS, SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC 2015), VOL 2, 2015, : 460 - 471
  • [27] Fuse: An Architecture for Smart Contract Fuzz Testing Service
    Chan, W. K.
    Jiang, Bo
    2018 25TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC 2018), 2018, : 707 - 708
  • [28] Recurrent Neural Networks for Fuzz Testing Web Browsers
    Sablotny, Martin
    Jensen, Bjorn Sand
    Johnson, Chris W.
    INFORMATION SECURITY AND CRYPTOLOGY (ICISC 2018), 2019, 11396 : 354 - 370
  • [29] Deep Learning Fuzz Testing Methods for Unstructured Case
    Yu, Haotian
    Li, Xiaoguang
    Du, Yuefeng
    2022 IEEE 22ND INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY, AND SECURITY COMPANION, QRS-C, 2022, : 772 - 773
  • [30] Investigating HTTP Covert Channels Through Fuzz Testing
    Holk, Kai
    Mazurczyk, Wojciech
    Zuppelli, Marco
    Caviglione, Luca
    19TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY, AND SECURITY, ARES 2024, 2024,