A Seed Scheduling Method With a Reinforcement Learning for a Coverage Guided Fuzzing

Choi, Gyeongtaek; Jeon, Seungho; Cho, Jaeik; Moon, Jongsub

doi:10.1109/access.2022.3233875

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…At present, coverage-based fuzzing tools do not differentiate the importance of different code coverage segments, irrespective of the functionality of code calls and their potential security impacts. Therefore, all input samples that aid in discovering new statements or code conversions will be saved for future mutations [20]. Although this approach may be deemed acceptable in software testing for achieving thorough code coverage, it is considered ineffective in identifying vulnerabilities.…”

Section: Frequency and Danger Based Path Value Evaluation Algorithmmentioning

confidence: 99%

LinFuzz: Program-Sensitive Seed Scheduling Greybox Fuzzing Based on LinUCB Algorithm

Su,

Xiong,

Wan

et al. 2024

IEEE Access

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The mutation-based grey box fuzz testing technique is one of the widely used dynamic vulnerability mining techniques. It generates test cases for testing by mutating input seeds. In the process of fuzz testing, the seed scheduling strategy and energy scheduling strategy impact the test results and efficiency. Existing seed scheduling strategies, however, only consider a few specific seed attributes and ignore contextual information during seed execution. This oversight makes it challenging to prioritize the selection of suitable seeds based on historical fuzz test results. Meanwhile, current methods for calculating coverage lack evaluation of software paths, which makes it easy to waste time on testing high-frequency and low-risk paths. This article proposes a new grey box fuzz scheme, LinFuzz, which transforms the seed scheduling problem into a contextual multi-arm bandit machine model. It utilizes the LinUCB algorithm to assess the value of seeds for scheduling by considering their historical execution information. At the same time, LinFuzz enhances the calculation method for fuzz testing path rewards and the seed energy scheduling algorithm. It allocates more energy for low-frequency paths in the testing program, thereby enhancing the exploration efficiency and path coverage ability of the testing tool. This article evaluated the proposed LinFuzz on 12 real programs in comparison with other open-source tools like AFL, AFLFast, FairFuzz, Neuzz, etc. The results show that under the same testing time budget, LinFuzz outperforms other tools in terms of vulnerability discovery quantity and code coverage ability. Compared with complex fuzz testing optimization algorithms, LinFuzz has lower memory consumption and time complexity.INDEX TERMS Coverage-guided fuzzing, LinUCB Algorithm, Seed Scheduling

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Section: Frequency and Danger Based Path Value Evaluation Algorithmmentioning

confidence: 99%

LinFuzz: Program-Sensitive Seed Scheduling Greybox Fuzzing Based on LinUCB Algorithm

Su,

Xiong,

Wan

et al. 2024

IEEE Access

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“…Recent studies have shown that GANs, especially StyleGAN2, 24 are highly effective in producing synthetic data for defect detection tasks 25 , 26 . Our research also leverages StyleGAN2 to create synthetic defects superimposed on real background images.…”

Section: Introductionmentioning

confidence: 99%

Enhancing titanium spacer defect detection through reinforcement learning-optimized digital twin and synthetic data generation

Mohanty,

Su,

2024

J. Electron. Imag.

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In the field of automatic defect detection, a major challenge in training accurate classifiers using supervised learning is the insufficient and limited diversity of datasets. Obtaining an adequate amount of image data depicting defective surfaces in an industrial setting can be costly and time-consuming. Furthermore, the collected dataset may suffer from selection bias, resulting in an underrepresentation of certain defect classes. Our research aims to address surface defect detection in titanium metal spacer rings by introducing an approach leveraging a digital twin framework. The behavior of the digital twin is optimized using a reinforcement learning (RL) algorithm. The optimized digital twin is then used to generate synthetic data, which is employed to train a spacer defect detection classifier. The classifier's performance is evaluated using real-world data. The results show that the model trained with synthetic data outperforms the one trained on a limited amount of real data. Our work highlights the potential of digital twin-based synthetic data generation and RL optimization in enhancing spacer surface defect detection and addressing the data scarcity challenge in the field. When the inspection network is trained solely using generated synthetic data, it achieves an inspection precision of 96.10%, with a recall of 85.77%. Incorporating real data alongside synthetic data for training further enhances performance, yielding an inspection precision of 93.07% and a recall of 94.20%. This surpasses the defect detection capabilities observed when training the inspection network exclusively with real data.

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Vulnerability detection through machine learning-based fuzzing: A systematic review

Bamohabbat Chafjiri,

Legg,

Hong

et al. 2024

Computers & Security

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A Seed Scheduling Method With a Reinforcement Learning for a Coverage Guided Fuzzing

Cited by 6 publications

References 22 publications

LinFuzz: Program-Sensitive Seed Scheduling Greybox Fuzzing Based on LinUCB Algorithm

LinFuzz: Program-Sensitive Seed Scheduling Greybox Fuzzing Based on LinUCB Algorithm

Enhancing titanium spacer defect detection through reinforcement learning-optimized digital twin and synthetic data generation

Vulnerability detection through machine learning-based fuzzing: A systematic review

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