Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156531
Title: American fuzzy lop (AFL) fuzzer
Authors: Wong, Cerdic Wei Kit
Keywords: Library and information science
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Wong, C. W. K. (2022). American fuzzy lop (AFL) fuzzer. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156531
Abstract: Vulnerability researchers can use American Fuzzy Lop (AFL), an evolutionary fuzzer, to find problems in their software during testing. As it is a critical component of the current vulnerabilities research, fuzzing is a crucial technique to know about. Fuzzing is the most historically effective approach for identifying flaws, according to an informal poll of vulnerability researchers. Improved fuzzers can help researchers to identify new flaws in software that is critical. Hence, greybox fuzzing is the most solid and basically strong strategy for mechanized programming testing. Regardless, a larger part of greybox fuzzers are not compelling in coordinated fluffing, for instance, towards confounded patches, just as towards dubious and basic destinations. To beat these impediments of greybox fuzzers, Directed Greybox Fuzzing (DGF) approaches were as of late proposed. Current DGFs are strong and productive methodologies that can rival Coverage-Based Fuzzers. In any case, DGFs disregard to achieve dependability among convenience and capability, and irregular transformations make it difficult to deal with complex ways.
URI: https://hdl.handle.net/10356/156531
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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