DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cha, Sang Kil | ko |
dc.contributor.author | Woo, Maverick | ko |
dc.contributor.author | Brumley, David | ko |
dc.date.accessioned | 2016-04-20T05:32:24Z | - |
dc.date.available | 2016-04-20T05:32:24Z | - |
dc.date.created | 2015-12-24 | - |
dc.date.created | 2015-12-24 | - |
dc.date.issued | 2015-05-20 | - |
dc.identifier.citation | IEEE Symposium on Security and Privacy, pp.725 - 741 | - |
dc.identifier.issn | 1081-6011 | - |
dc.identifier.uri | http://hdl.handle.net/10203/205005 | - |
dc.description.abstract | We present the design of an algorithm to maximize the number of bugs found for black-box mutational fuzzing given a program and a seed input. The major intuition is to leverage white-box symbolic analysis on an execution trace for a given program-seed pair to detect dependencies among the bit positions of an input, and then use this dependency relation to compute a probabilistically optimal mutation ratio for this program-seed pair. Our result is promising: we found an average of 38.6% more bugs than three previous fuzzers over 8 applications using the same amount of fuzzing time. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Program-Adaptive Mutational Fuzzing | - |
dc.type | Conference | - |
dc.identifier.wosid | 000380537900043 | - |
dc.identifier.scopusid | 2-s2.0-84945180591 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 725 | - |
dc.citation.endingpage | 741 | - |
dc.citation.publicationname | IEEE Symposium on Security and Privacy | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Fairmont San Jose | - |
dc.identifier.doi | 10.1109/SP.2015.50 | - |
dc.contributor.localauthor | Cha, Sang Kil | - |
dc.contributor.nonIdAuthor | Woo, Maverick | - |
dc.contributor.nonIdAuthor | Brumley, David | - |
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