Hardware Support to Improve Fuzzing Performance and Precision-Reference-Cited by-同舟云学术

Hardware Support to Improve Fuzzing Performance and Precision

Published:2021-11-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security
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Author:

Ding Ren¹,Kim Yonghae¹,Sang Fan¹,Xu Wen¹,Saileshwar Gururaj¹,Kim Taesoo¹

Affiliation:

1. Georgia Institute of Technology, Atlanta, GA, USA

Funder

NSF

ONR

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3460120.3484573

Reference64 articles.

1. Apple. 2020. M1: Apple Neural Engine. https://www.apple.com/newsroom/2020/11/apple-unleashes-m1/. Apple. 2020. M1: Apple Neural Engine. https://www.apple.com/newsroom/2020/11/apple-unleashes-m1/.

2. ARM. 2009. CoreSight Components Technical Reference Manual. https://developer.arm.com/documentation/ddi0314/h/. ARM. 2009. CoreSight Components Technical Reference Manual. https://developer.arm.com/documentation/ddi0314/h/.

3. REDQUEEN: Fuzzing with Input-to-State Correspondence

4. UC Berkeley. 2016. Rocket Chip Generator. https://github.com/chipsalliance/rocket-chip. UC Berkeley. 2016. Rocket Chip Generator. https://github.com/chipsalliance/rocket-chip.

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