Urban areas have higher heterogeneity compared to natural areas,it is crucial to assessfine-resolution land cover products and discover how they differ in urban areas so that they can be efficiently used for various a...Urban areas have higher heterogeneity compared to natural areas,it is crucial to assessfine-resolution land cover products and discover how they differ in urban areas so that they can be efficiently used for various application scenarios.In this study,five typical cities in China were chosen as study areas to evaluate four commonly used 30 m land cover products:GLC_FCS30-2020,FROM-GLC30-2017,Globeland30-2020,and CLCD-2019.We analyzed the reliability of these four products using validation samples as well as by examining their area and spatial pattern consistency.Given the limitations of traditional accuracy assessments at the macro level,we added a local area evaluation to further examine the classification details in these products.The macro results indicated that four land cover products within urban areas have a similar overall accuracy,surpassing 76%,but there was a low consistency among them,ranging from 42.21%to 61.13%.The local accuracy assessment illustrated that GLC_FCS30-2020 and FROM-GLC30-2017 performed well in reflecting the intricate details of the city,however,the four products exhibited varying degrees of misclassifications and omissions.These phenomena suggest that more sophisticated algorithms are needed to consider urban particularities sincefine-resolution land cover products may fail to capture complex urban details.展开更多
Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and effectiveness.Mu...Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and effectiveness.Mutation-based greybox fuzzing generates input cases by mutating the input seed,i.e.,applying a sequence of mutation operators to randomly selected mutation positions of the seed.However,existing fruitful research work focuses on scheduling mutation operators,leaving the schedule of mutation positions as an overlooked aspect of fuzzing efficiency.This paper proposes a novel greybox fuzzing method,PosFuzz,that statistically schedules mutation positions based on their historical performance.PosFuzz makes use of a concept of effective position distribution to represent the semantics of the input and to guide the mutations.PosFuzz first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation operator.It then leverages two sampling methods in different mutating stages to select the positions from the distribution.We have implemented PosFuzz on top of AFL,AFLFast and MOPT,called Pos-AFL,-AFLFast and-MOPT respectively,and evaluated them on the UNIFUZZ benchmark(20 widely used open source programs)and LAVA-M dataset.The result shows that,under the same testing time budget,the Pos-AFL,-AFLFast and-MOPT outperform their counterparts in code coverage and vulnerability discovery ability.Compared with AFL,AFLFast,and MOPT,PosFuzz gets 21%more edge coverage and finds 133%more paths on average.It also triggers 275%more unique bugs on average.展开更多
基金supported by the National Natural Science Foundation of China[42090012].
文摘Urban areas have higher heterogeneity compared to natural areas,it is crucial to assessfine-resolution land cover products and discover how they differ in urban areas so that they can be efficiently used for various application scenarios.In this study,five typical cities in China were chosen as study areas to evaluate four commonly used 30 m land cover products:GLC_FCS30-2020,FROM-GLC30-2017,Globeland30-2020,and CLCD-2019.We analyzed the reliability of these four products using validation samples as well as by examining their area and spatial pattern consistency.Given the limitations of traditional accuracy assessments at the macro level,we added a local area evaluation to further examine the classification details in these products.The macro results indicated that four land cover products within urban areas have a similar overall accuracy,surpassing 76%,but there was a low consistency among them,ranging from 42.21%to 61.13%.The local accuracy assessment illustrated that GLC_FCS30-2020 and FROM-GLC30-2017 performed well in reflecting the intricate details of the city,however,the four products exhibited varying degrees of misclassifications and omissions.These phenomena suggest that more sophisticated algorithms are needed to consider urban particularities sincefine-resolution land cover products may fail to capture complex urban details.
基金This research was supported by National Key R&D Program of China(2022YFB3103900)National Natural Science Foundation of China(62032010,62202462)Strategic Priority Research Program of the CAS(XDC02030200).
文摘Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and effectiveness.Mutation-based greybox fuzzing generates input cases by mutating the input seed,i.e.,applying a sequence of mutation operators to randomly selected mutation positions of the seed.However,existing fruitful research work focuses on scheduling mutation operators,leaving the schedule of mutation positions as an overlooked aspect of fuzzing efficiency.This paper proposes a novel greybox fuzzing method,PosFuzz,that statistically schedules mutation positions based on their historical performance.PosFuzz makes use of a concept of effective position distribution to represent the semantics of the input and to guide the mutations.PosFuzz first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation operator.It then leverages two sampling methods in different mutating stages to select the positions from the distribution.We have implemented PosFuzz on top of AFL,AFLFast and MOPT,called Pos-AFL,-AFLFast and-MOPT respectively,and evaluated them on the UNIFUZZ benchmark(20 widely used open source programs)and LAVA-M dataset.The result shows that,under the same testing time budget,the Pos-AFL,-AFLFast and-MOPT outperform their counterparts in code coverage and vulnerability discovery ability.Compared with AFL,AFLFast,and MOPT,PosFuzz gets 21%more edge coverage and finds 133%more paths on average.It also triggers 275%more unique bugs on average.
