Sudden Oak Death in California

Swiecki, Tedmund J.; Bernhardt, Elizabeth A.

doi:10.1007/978-3-319-24744-1_25

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…While Botryosphaeriaceae fungi, including B. dothidea, have been retrieved on manzanita species (Brooks and Ferrin, 1994;Swiecki and Bernhardt, 2003), to the authors' knowledge this is the first report of N. australe being recovered from branch cankers in Arctostaphylos hosts in California, and on any naturally occurring species in Santa Barbara County. In November 2016, pathogenicity of N. australe was tested using approximately 2-year old A. glauca plants in the greenhouse.…”

mentioning

confidence: 89%

First Report of Neofusicoccum australe Causing Canker Disease and Branch Dieback on Arctostaphylos glauca in California

2019

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mentioning

confidence: 89%

First Report of Neofusicoccum australe Causing Canker Disease and Branch Dieback on Arctostaphylos glauca in California

2019

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“…Several studies have addressed the optimization of seed scheduling problems by introducing fitness objectives. Hongfuzz [4] adopts a strategy of selecting seed files in order, which is simple and intuitive, but may lack targeted optimization. LibFuzzer [5] prioritizes selecting seeds that can trigger more uncovered program blocks, emphasizing expanding code coverage to discover more potential problem areas.…”

Section: A Coverage-guided Grey-box Fuzzingmentioning

confidence: 99%

“…The classic seed scheduling strategy primarily relies on a single indicator to calculate seed fitness for guiding seed scheduling and prioritizing sorting [4], [5], [6], [7]. This method evaluates the relative value of candidate seeds in the test sequence by establishing a concise and intuitive measurement standard.…”

Section: Introductionmentioning

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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“…Moreover, several real-world programs fail to compile under DTA, increasing deployability concerns. Thus, most widely-deployed greybox fuzzers (e.g., AFL [3], libFuzzer [21], and honggfuzz [22]) eschew DTA in favor of higher fuzzing throughput.…”

mentioning

confidence: 99%

datAFLow: Towards a Data-Flow-Guided Fuzzer

Herrera¹,

Payer²,

Hosking³

2022

Proceedings FUZZING 2022 - 1st International Fuzzing Workshop

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We perform a preliminary evaluation of DATAFLOW, comparing fuzzers driven by control flow, taint analysis (both approximate and exact), and data flow. Our initial results suggest that, so far, pure coverage remains the best coverage metric for uncovering bugs in most targets we fuzzed (72 % of them). However, data-flow coverage does show promise in targets where control flow is decoupled from semantics (e.g., parsers). Further evaluation and analysis on a wider range of targets is required.

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Sudden Oak Death in California

Cited by 8 publications

References 41 publications

First Report of Neofusicoccum australe Causing Canker Disease and Branch Dieback on Arctostaphylos glauca in California

First Report of Neofusicoccum australe Causing Canker Disease and Branch Dieback on Arctostaphylos glauca in California

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

datAFLow: Towards a Data-Flow-Guided Fuzzer

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