Böhme-Rezeption am Hof von Christian August von Pfalz-Sulzbach

Zeller, Rosmarie

doi:10.1515/9783110288612.125

Cited by 1 publication

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“…For fuzzing for web application vulnerabilities, spider technology is mainly used to collect and organize test input data, and the test data set is constructed manually. For fuzzing for binary programs, publicly available datasets include AFLSmart [72] and Fuzzingbook [73], which contain over 1,000 binary programs and their corresponding seed files that can be directly obtained from the official website. The quality of the dataset directly affects the training effect of the machine learning model and the performance of the vulnerability detection model.…”

Section: Dataset Standardizationmentioning

confidence: 99%

Machine Learning-Based Fuzz Testing Techniques: A Survey

Zhang,

et al. 2024

IEEE Access

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Fuzz testing is a vulnerability discovery technique that tests the robustness of target programs by providing them with unconventional data. With the rapid increase in software quantity, scale and complexity, traditional fuzzing has revealed issues such as incomplete logic coverage, low automation level and insufficient test cases. Machine learning, with its exceptional capabilities in data analysis and classification prediction, presents a promising approach for improve fuzzing. This paper investigates the latest research results in fuzzing and provides a systematic review of machine learning-based fuzzing techniques. Firstly, by outlining the workflow of fuzzing, it summarizes the optimization of different stages of fuzzing using machine learning. Specifically, it focuses on the application of machine learning in the preprocessing phase, test case generation phase, input selection phase and result analysis phase. Secondly, it mentally focuses on the optimization methods of machine learning in the process of mutation, generation and filtering of test cases and compares and analyzes its technical principles. Furthermore, it analyzes the performance gains brought by applying machine learning techniques to fuzzing, mainly including coverage, vulnerability detection capability, efficiency and effectiveness of test cases. Lastly, it concludes by summarizing the challenges and difficulties in combining machine learning with fuzzing and presents prospects for future trends in this field.

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Section: Dataset Standardizationmentioning

confidence: 99%

Machine Learning-Based Fuzz Testing Techniques: A Survey

Zhang,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Böhme-Rezeption am Hof von Christian August von Pfalz-Sulzbach

Cited by 1 publication

References 0 publications

Machine Learning-Based Fuzz Testing Techniques: A Survey

Machine Learning-Based Fuzz Testing Techniques: A Survey

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